Statistical analysis and interpretation of data
Pmc5037948basic statistical tools in research and data analysiszulfiqar ali and s bala bhaskar1department of anaesthesiology, division of neuroanaesthesiology, sheri kashmir institute of medical sciences, soura, srinagar, jammu and kashmir, india1department of anaesthesiology and critical care, vijayanagar institute of medical sciences, bellary, karnataka, indiaaddress for correspondence: dr. 2016 october; 60(10): article has been cited by other articles in ctstatistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data words: basic statistical tools, degree of dispersion, measures of central tendency, parametric tests and non-parametric tests, variables, varianceintroductionstatistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population. 1] this requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature. Hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [figure 1]. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[4] use a random sample of data taken from a population to describe and make inferences about the whole population. Median[6] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. In a positively skewed distribution [figure 3], the mass of the distribution is concentrated on the left of the figure leading to a longer right 3curves showing negatively skewed and positively skewed distributioninferential statisticsin inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. 3p values with interpretationif p value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (h0) is rejected [table 4]. 12]table 4illustration for null hypothesisparametric and non-parametric tests numerical data (quantitative variables) that are normally distributed are analysed with parametric tests. 13]two most basic prerequisites for parametric statistical analysis are:The assumption of normality which specifies that the means of the sample group are normally distributedthe assumption of equal variance which specifies that the variances of the samples and of their corresponding population are r, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[14] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical tric tests the parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The commonly used parametric tests are the student's t-test, analysis of variance (anova) and repeated measures t's t-teststudent's t-test is used to test the null hypothesis that there is no difference between the means of the two groups. Simplified formula for the f statistic is:where msb is the mean squares between the groups and msw is the mean squares within ed measures analysis of varianceas with anova, repeated measures anova analyses the equality of means of three or more groups. Using a standard anova in this case is not appropriate because it fails to model the correlation between the repeated measures: the data violate the anova assumption of independence. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in table 5analogue of parametric and non-parametric testsmedian test for one sample: the sign test and wilcoxon's signed rank testthe sign test and wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference testthis test examines the hypothesis about the median θ0 of a population. If the observed value is equal to the reference value (θ0), it is eliminated from the the null hypothesis is true, there will be an equal number of + signs and − sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the on's signed rank testthere is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs.
Analysis and interpretation of data in statistics
Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the on's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank -whitney testit is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the –whitney test compares all data (xi) belonging to the x group and all data (yi) belonging to the y group and calculates the probability of xi being greater than yi: p (xi > yi). The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test eere testin contrast to kruskal–wallis test, in jonckheere test, there is an a priori ordering that gives it a more statistical power than the kruskal–wallis test. 13]tests to analyse the categorical data chi-square test, fischer's exact test and mcnemar's test are used to analyse the categorical or nominal variables. The chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i. It is calculated by the sum of the squared difference between observed (o) and the expected (e) data (or the deviation, d) divided by the expected data by the following formula:a yates correction factor is used when the sample size is small. If the outcome variable is dichotomous, then logistic regression is res available for statistics, sample size calculation and power analysisnumerous statistical software systems are available currently. The commonly used software systems are statistical package for the social sciences (spss – manufactured by ibm corporation), statistical analysis system ((sas – developed by sas institute north carolina, united states of america), r (designed by ross ihaka and robert gentleman from r core team), minitab (developed by minitab inc), stata (developed by statacorp) and the ms excel (developed by microsoft). A few are:Summaryit is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based ial support and cts of interestthere are no conflicts of nces1. Most important methods for statistical data the information age, data is no longer scarce – it’s overpowering. The key is to sift through the overwhelming volume of data available to organizations and businesses and correctly interpret its implications. But to sort through all this information, you need the right statistical data analysis the current obsession over “big data,” analysts have produced a lot of fancy tools and techniques available to large organizations. However, there are a handful of basic data analysis tools that most organizations aren’t using…to their suggest starting your data analysis efforts with the following five fundamentals – and learn to avoid their pitfalls – before advancing to more sophisticated arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list. The mean is useful in determining the overall trend of a data set or providing a rapid snapshot of your data. In some data sets, the mean is also closely related to the mode and the median (two other measurements near the average). However, in a data set with a high number of outliers or a skewed distribution, the mean simply doesn’t provide the accuracy you need for a nuanced decision. Standard standard deviation, often represented with the greek letter sigma, is the measure of a spread of data around the mean. A high standard deviation signifies that data is spread more widely from the mean, where a low standard deviation signals that more data align with the mean. In a portfolio of data analysis methods, the standard deviation is useful for quickly determining dispersion of data like the mean, the standard deviation is deceptive if taken alone. For example, if the data have a very strange pattern such as a non-normal curve or a large amount of outliers, then the standard deviation won’t give you all the information you sion models the relationships between dependent and explanatory variables, which are usually charted on a scatterplot. For example, an outlying data point may represent the input from your most critical supplier or your highest selling product. As an illustration, examine a picture of anscombe’s quartet, in which the data sets have the exact same regression line but include widely different data points. Sample size measuring a large data set or population, like a workforce, you don’t always need to collect information from every member of that population – a sample does the job just as well. Using proportion and standard deviation methods, you are able to accurately determine the right sample size you need to make your data collection statistically studying a new, untested variable in a population, your proportion equations might need to rely on certain assumptions. This error is then passed along to your sample size determination and then onto the rest of your statistical data analysis. Hypothesis commonly called t testing, hypothesis testing assesses if a certain premise is actually true for your data set or population. In data analysis and statistics, you consider the result of a hypothesis test statistically significant if the results couldn’t have happened by random chance.
Another common error is the hawthorne effect (or observer effect), which happens when participants skew results because they know they are being l, these methods of data analysis add a lot of insight to your decision-making portfolio, particularly if you’ve never analyzed a process or data set with statistics before. Once you master these fundamental techniques for statistical data analysis, then you’re ready to advance to more powerful data analysis learn more about improving your statistical data analysis through powerful data visualization, click the button below to download our free guide, “5 tips for security data analysis” and start turning your abstract numbers into measurable y policysite mapdesign by hinge© big sky wikipedia, the free to: navigation, of a series on atory data analysis • information ctive data ptive statistics • inferential tical graphics • analysis • munzner • ben shneiderman • john w. Tukey • edward tufte • fernanda viégas • hadley ation graphic chart • bar ram • t • pareto chart • area l chart • run -and-leaf display • multiple • unk • visual sion analysis • statistical ational cal analysis · analysis · /long-range potential · lennard-jones potential · yukawa potential · morse difference · finite element · boundary e boltzmann · riemann ative particle ed particle ation · gibbs sampling · metropolis algorithm. Body · v · ulam · von neumann · galerkin · analysis, also known as analysis of data or data analytics, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. 1] in statistical applications data analysis can be divided into descriptive statistics, exploratory data analysis (eda), and confirmatory data analysis (cda). Eda focuses on discovering new features in the data and cda on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. Science process flowchart from "doing data science", cathy o'neil and rachel schutt, is refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. John tukey defined data analysis in 1961 as: "procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data. Data is necessary as inputs to the analysis are specified based upon the requirements of those directing the analysis or customers who will use the finished product of the analysis. The general type of entity upon which the data will be collected is referred to as an experimental unit (e. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. Phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i. The need for data cleaning will arise from problems in the way that data is entered and stored. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data,[5] deduplication, and column segmentation. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. 9][10] the process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics such as the average or median may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data. Formulas or models called algorithms may be applied to the data to identify relationships among the variables, such as correlation or causation. In general terms, models may be developed to evaluate a particular variable in the data based on other variable(s) in the data, with some residual error depending on model accuracy (i. For example, regression analysis may be used to model whether a change in advertising (independent variable x) explains the variation in sales (dependent variable y).
Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results. Data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy. Article: data the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. Determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays such as tables and charts to help communicate key messages contained in the data. Scatterplot illustrating correlation between two variables (inflation and unemployment) measured at points in stephen few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the -series: a single variable is captured over a period of time, such as the unemployment rate over a 10-year period. Also: problem jonathan koomey has recommended a series of best practices for understanding quantitative data. Problems into component parts by analyzing factors that led to the results, such as dupont analysis of return on equity. They may also analyze the distribution of the key variables to see how the individual values cluster around the illustration of the mece principle used for data consultants at mckinsey and company named a technique for breaking a quantitative problem down into its component parts called the mece principle. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false. Hypothesis testing involves considering the likelihood of type i and type ii errors, which relate to whether the data supports accepting or rejecting the sion analysis may be used when the analyst is trying to determine the extent to which independent variable x affects dependent variable y (e. This is an attempt to model or fit an equation line or curve to the data, such that y is a function of ary condition analysis (nca) may be used when the analyst is trying to determine the extent to which independent variable x allows variable y (e. Whereas (multiple) regression analysis uses additive logic where each x-variable can produce the outcome and the x's can compensate for each other (they are sufficient but not necessary), necessary condition analysis (nca) uses necessity logic, where one or more x-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not ical activities of data users[edit]. May have particular data points of interest within a data set, as opposed to general messaging outlined above. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points. Some concrete conditions on attribute values, find data cases satisfying those data cases satisfy conditions {a, b, c... Derived a set of data cases, compute an aggregate numeric representation of those data is the value of aggregation function f over a given set s of data cases? Data cases possessing an extreme value of an attribute over its range within the data are the top/bottom n data cases with respect to attribute a? A set of data cases, rank them according to some ordinal is the sorted order of a set s of data cases according to their value of attribute a? Rank the cereals by a set of data cases and an attribute of interest, find the span of values within the is the range of values of attribute a in a set s of data cases? A set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the is the distribution of values of attribute a in a set s of data cases? Any anomalies within a given set of data cases with respect to a given relationship or expectation, e. A set of data cases, find clusters of similar attribute data cases in a set s of data cases are similar in value for attributes {x, y, z, ... A set of data cases and two attributes, determine useful relationships between the values of those is the correlation between attributes x and y over a given set s of data cases? A set of data cases, find contextual relevancy of the data to the data cases in a set s of data cases are relevant to the current users' context? To effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data ing fact and opinion[edit].
Are entitled to your own opinion, but you are not entitled to your own patrick ive analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. In his book psychology of intelligence analysis, retired cia analyst richards heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques. Analysts apply a variety of techniques to address the various quantitative messages described in the section ts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock. 21] the different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and ics and business intelligence[edit]. Article: ics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. It is a subset of business intelligence, which is a set of technologies and processes that use data to understand and analyze business performance. Activities of data visualization education, most educators have access to a data system for the purpose of analyzing student data. 23] these data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses. Section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a wikipedia l data analysis[edit]. Most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not for common-method choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. Quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. During this analysis, one inspects the variances of the items and the scales, the cronbach's α of the scales, and the change in the cronbach's alpha when an item would be deleted from a scale[27]. Assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase. Should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in possible data distortions that should be checked are:Dropout (this should be identified during the initial data analysis phase). Nonresponse (whether this is random or not should be assessed during the initial data analysis phase). It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis characteristics of the data sample can be assessed by looking at:Basic statistics of important ations and -tabulations[31]. The final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are , the original plan for the main data analyses can and should be specified in more detail or order to do this, several decisions about the main data analyses can and should be made:In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method? The case of missing data: should one neglect or impute the missing data; which imputation technique should be used? Is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:[34]. The main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are atory data analysis should be interpreted carefully. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. There are two main ways of doing this:Cross-validation: by splitting the data in multiple parts we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as ivity analysis: a procedure to study the behavior of a system or model when global parameters are (systematically) varied.
A database system endorsed by the united nations development group for monitoring and analyzing human – data mining framework in java with data mining oriented visualization – the konstanz information miner, a user friendly and comprehensive data analytics – a visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine – free software for scientific data – fortran/c data analysis framework developed at cern. A programming language and software environment for statistical computing and – c++ data analysis framework developed at and pandas – python libraries for data ss ing (statistics). Presentation l signal case atory data inear subspace ay data t neighbor ear system pal component ured data analysis (statistics). Clean data in crm: the key to generate sales-ready leads and boost your revenue pool retrieved 29th july, 2016. William newman (1994) "a preliminary analysis of the products of hci research, using pro forma abstracts". How data systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Manual on presentation of data and control chart analysis, mnl 7a, isbn rs, john m. Data analysis: an introduction, sage publications inc, isbn /sematech (2008) handbook of statistical methods,Pyzdek, t, (2003). Data analysis: testing for association isbn ries: data analysisscientific methodparticle physicscomputational fields of studyhidden categories: wikipedia articles with gnd logged intalkcontributionscreate accountlog pagecontentsfeatured contentcurrent eventsrandom articledonate to wikipediawikipedia out wikipediacommunity portalrecent changescontact links hererelated changesupload filespecial pagespermanent linkpage informationwikidata itemcite this a bookdownload as pdfprintable version. 0 - a guide for using statistics for evidence based policy, issue released at 11:30 am (canberra time) 20/10/2010 first ydownloadsexplanatory notesrelated informationpast & future javascript to print pagesprint ts about this release expanded e, interpret and evaluate statistical e, interpret and evaluate statistical process of data analysis, is the process of turning data into meaningful information. Although there are no hard and fast rules for how to analyse statistical data, ensuring that you have a methodical and systematic approach is vital to ensuring your analysis is accurate. Poor quality analysis can lead you to draw incorrect and inappropriate 1 identify the issues or questions you require information about, specify objectives, and formulate ensure the analysis conducted is appropriate for addressing the underlying objective, it is vital that you understand the issue you are investigating. You can then formulate a set of questions which you’re seeking answers and specify your objectives for analysing the data. The analysis will throw light on the circumstances between those staying and those leaving school, and help introduce a program aimed at encouraging students to remain in schools until year is also a good idea to formulate a set of expectations for what the data might reveal. Developing an understanding of why certain patterns might emerge in the data and what it might mean for your analysis, will help you analyse the data and draw 2 determine appropriate analytical techniques and undertake data ining which analytical techniques are appropriate for investigating the data is necessary before any analysis takes place. Keep in mind that some analytical techniques are not always appropriate for all sets of data. It is important to ensure that appropriate techniques are used in order to avoid misinterpretation or misleading results. Most of these statistical measures can be calculated automatically in different analytical techniques can be broadly broken down into summary statistical measures and graphical analysis, however these are often used in cal analysis is a useful way to gain an instant picture of the distribution of the data and identifying any relationships in the data that require further investigation. A range of graphical techniques can be used to present data in a pictorial format. For example, column graphs, row graphs, dot graphs and line way of summarising data is to produce a frequency distribution table or graph. A frequency table is a grouping of data into categories showing the number of observations in each category. It may also be appropriate to plot relative frequencies to show the percentage of the population within each class interval – which enables the different sizes to be directly y statistical ating summary statistics will assist you to understand the distribution of the data. These summary measures are useful for comparing information and are more precise than graphical analysis. Summary statistics assist you to develop an understanding of:• the centre of a set of data. However, these measures of location can’t tell the whole story about the distribution of the data. It is possible for two data sets to have the same mean but vastly different distributions. The range, inter-quartile range, standard deviation, and variance are useful measures of variability or the spread of the are also a range of analytical techniques that can enable you to gain a deeper understanding of the data. This can involve analysing the data to determine change over time; comparison between groups; comparing like with like; and relationships between variables. Modelling techniques such as linear regression, logistic regression, and time series analysis are some ways to explore these relationships. Assistance can be sought from experienced analysts when undertaking complex statistical 3 assess the results of analysis against the objectives and you have analysed, and computed some statistics from the data and feel that you have a good grasp for what the data is saying, you can then look at drawing appropriate conclusions about the data.
Your analysis may provide you with the basis for describing what happened but there may be many possible reasons for why this has occurred. You may need to seek clarification through further analysis and research to ensure the conclusions you draw are things to consider when drawing conclusions may be:• what are the main results or conclusions that can be drawn? 4 review the objectives, recommence data analysis cycle as there are still questions unanswered, you may need to begin the data analysis cycle you’re interested in learning more about data analysis, the abs delivers a specialised training course on ‘analysing survey data made simple’. The course covers these basic principles for analysing data as well as exploring more complex data analysis us pagenext page last updated 20 october inflation ation for small tical tical methods & ation consultancy to help us improve our website?