Different data analysis techniques

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 associates. 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 a true content the new digital re is in the business of content marketing—and, inevitably, so is your brand. Data means nothing to marketers without the proper tools to interpret and analyze that data. Learn techniques to get more rich, useful information out of your data using excel, and take the next step to build a rich profile of data-driven marketing a spreadsheet opened in front of you, you stare at mountains of raw data without a clue what to do, feeling like you’re drowning in the ’ve heard marketers talking about data-driven marketing and “big data,” and having learned that many companies such as facebook are using third party data, you sent out surveys and collected tons of data in order to do some of that “data-driven marketing” you’ve heard so much about. However, data is useless if you don’t know how to analyze it correctly and market research plays a big part at iacquire, there is always a bunch of data collected to learn about the market. After the data is collected, i think about what possible findings and conclusions i can get and analyze the data based on the possible outcomes. In this blog post, i will introduce to you the seven most common and useful data analysis techniques for survey analysis, and then walk you through their processes in excel. Note: the following examples will be shown in excel analysis technique 1: frequency distribution (histogram in excel).

Distribution is a simple data analysis technique which allows you to get a big picture of the data. For example: for variable of “age,” you can use frequency distribution to figure out how many people in the survey are aged 18 to 25, and how many are aged 26 to 33, etc… histogram is a great tool in excel to recognize frequency distribution in data like to use the histogram feature in excel:2. For nominal data, such as “gender” and “marital status” you need to recode the variables into numbers, such as “male = 1” and “female = 0”, etc. You can also use pivot tables to compute the frequencies of the nominal data which will be easier. This technique will be introduced analysis technique 2: descriptive the frequency distribution we can figure out the frequency of the values observed, as shown in the “age example” above. We can use the measures of central tendency and dispersion to learn more about the data for “age. It is the most popular measure of central tendency, especially when the data set does not have an outlier. It is useful when the data set has an outlier and values distribute very unevenly. It is useful when the data is non-numeric or when asked to find the most popular and standard deviation are the basics measures of dispersion.

Click “summary statistics” output will look like this:From this, you can easily learn about the central tendency and dispersion of the values for the analysis technique 3: comparing means – statistical up! For example, if the mean for variable 1 is 20 and the mean for variable 2 is 28, you may say the means are different. T-tests may show you that they are not significantly different, however, and you can’t base your conclusion on the means’ difference since the difference in the sample is not representative for the excel, there are three types of t-tests: t-test: paired two sample for means, t­test: two­sample assuming equal variances and t­test: two­sample assuming unequal variances. Usually, we only need to use t-test: paired two sample for means and t­test: two­sample assuming unequal t-test, also called dependent t test, is used when the data of variable 1 and data of variable 2 were collected in parallel from each individual, such as “before versus after” cases. Suppose the marketer collected the ratings data before changing the product packaging and after changing it. The way to do a two-sample t-test is similar to the paired t-test, except that you need to choose “t­test: two­sample assuming unequal variances” in the tool analysis technique 4: cross-tabulation (pivot table in excel). Tabulation, also called pivot table in excel, is one of the most popular techniques for data analysis. You can also test the statistical significance using chi-test for the cross-tabulation : if you edit the data after the pivot table and the graph are generated, you need to refresh it. Analysis technique 5: ations are used when you want to know about the relationship between two variables.

They are usually to use correlations in excel:Note: you can only use correlations for numeric data in excel. Click in the box next to “array 2” and highlight the second column of analysis technique 6: linear sion is a more accurate way to test the relationship between the variables compared with correlations since it shows the goodness of fit (adjusted r square) and the statistical testing for the variables. To do regression analysis in excel:I use the example of a multiple regression of ratings for product quality and ratings for packaging on the willingness to pay. Therefore, when making marketing decision, marketers should focus on the product quality according to this survey analysis technique 7: text the survey, there are always some open questions which will allow respondents to fill in their own answers. In this example, it indicates the respondents’ job titles are related to marketing, manager, seo and director, learning these techniques for data analysis, i bet you won’t feel like drowning any more when looking into the spreadsheet with tons of are some useful resources for data analysis techniques:Computer help : how to make a pivot table in spss for t uction to regression analysis with excel. Just what i was looking for to gain some insight on data analysis techniques for my research. Am working as a sr engineer quality assurance for pricol technologies,i have interest in data analysis,how can my make my career more effective. If you are a beginner of data analysis, i will recommend you learn and practice the techniques in this post and learn more about advanced excel skills. When you become more advanced in data analysis, you can learn sql or sas, with what you can deal with bigger datasets.

Such a useful and very interesting stuff to do in every research and data analysis you wanna do! Thank you very much for the very organized data analysis tips i learned a lot from it. But things can’t learn easily i will need few more attempts and patience to master this kanth adepu says:July 15, 2014 at 9:52 jiafeng li, thanks for this info about data analysis techniques. I am alergic to data, but i need to write some exams on quantitative analysis, reason why i am on this anrao, says:June 13, 2015 at 10:08 analysers ask:how to compute adjusted r is the use of paired t we always assume unequal variances for t 3, 2015 at 10:51 useful article for me as monitoring and evaluation by authorrelated postspopular play games on smartphones more often than men ts off on women play games on smartphones more often than men do – infographic. Seo analysis : online shopping behavior in the digital : the secret to successful marketing ts off on mart: the secret to successful marketing alone won’t guarantee better marketing ts off on data alone won’t guarantee better marketing persona-driven keyword research play games on smartphones more often than men ts off on women play games on smartphones more often than men do – most powerful competitor research tool you’re not using (yet). Thank you very much for your of data analysis view this video please enable javascript, and consider upgrading to a web browser g... The course by pwcdata-driven decision making1433 ratingstry the course for freethis coursevideo transcriptpwcdata-driven decision making1433 ratingscourse 1 of 5 in the specialization data analysis and presentation skills: the pwc approachwelcome to data-driven decision making. In this course you'll get an introduction to data analytics and its role in business decisions. You'll also be introduced to a framework for conducting data analysis and what tools and techniques are commonly used.

Finally, you'll have a chance to put your knowledge to work in a simulated business course was created by pricewaterhousecoopers llp with an address at 300 madison avenue, new york, new york, the lessondata analysis techniques and toolsin this module we will describe some of the tools for data analytics and some of the key technologies for data analysis. Finally we will identify a variety of tools and languages used and consider when those tools are best of data analysis techniques10:01meet the instructorsalex mannellaalumni / former principal0:00[music] 0:11welcome back. In this video we're going to look at the different types of analyses you can perform once you have identified the business problem or opportunity, developed a hypothesis and collected relevant data. As processing capacity continues to increase, it has opened the door to a broad range of advanced algorithms and modeling techniques. 0:36we will discuss a series of analytical techniques and how they are used in the real world. In the course materials, you have access to a quick reference sheet that lists out all techniques for easy future reference. I'm lorie wijntjes, managing director in our data and analytics practice with almost 30 years experience as a statistician. At pwc, i have worked on a wide variety of business problems involving predictive analytics, data management, statistical sampling, and survey design. In this video i will give you a high level overview of the different types of analysis that you can perform on data.

As part of the course you will find supplemental reading that covers each of these analysis types and how they are used. Now, it's important to keep in mind that the analysis you choose to perform will depend on a couple of things. First, the problem you are trying to solve, and second, the data you can use to solve that problem. 1:47cluster analysis is when you group a set of objects in a way that objects in the same group or cluster are more similar to one another than those in the other clusters. Cluster analysis is often used in market research when working with data from focus groups and surveys. A cluster analysis can be used to segment a population of consumers into market groups to better understand the relationships between different groups of consumers. Decision tree analysis is often used to assist healthcare practitioners considering varying treatments along with each one's associated costs and probability of a successful outcome. For example, healthcare providers can use this analysis to assess options and deliver more cost effective treatments that minimize the risk of hospital readmission. This type of analysis can help detect what aspects of the independent variables are related to the dependent variables.

When we receive the data, sets that are fairly wide, meaning that they had more variables in observations or records. Factor analysis can help identify that reduced subset of variables, meaning some of those variables represent similar relationships as those not included, but perhaps in a stronger way. 4:31regression analysis is a statistical process for estimating relationships between a dependent variable and one or more independent variables. This type of analysis helps you understand how the value of a dependent variable changes when any one of the independent variables change. This type of analysis can be used to assess risk, and also assist with determining pricing for various automobile insurance products. Multivariate analysis is the observation and analysis of more than one statistical outcome variable at a time. This often includes as a first step correlation analysis, which can help you understand and visualize relationships between pairs of variables. 6:08segmentation analysis divides a broad category into subsets that have or are perceived to have common features, needs, interests, or priorities. 6:17often, segmentation analysis is used to better understand customer needs by diving a large number of individuals into smaller groups based on a logical scheme.

Segmentation analysis could help the bank gain market share by identifying key customer segments and developing product recommendations for those that are more likely to use mobile banking. Sentiment analysis is a process of identifying and categorizing opinions expressed in a piece of text to determine whether the writer's attitude towards a topic or issue is positive, negative, or neutral. 8:29time series analysis can be used to design a methodology to identify the factors affecting airline passenger demand on routes by leveraging macroeconomic, demographic, and other external data, at a local, state, and national level. Time series analysis comprises methods for analyzing data that are collected over time to extract meaningful statistics. Because of this sequential nature of the data, special statistical techniques accounting for the dynamic nature of the data are required. And as you can see there are many different analytical techniques that can be used to address a problem or opportunity. 9:41in the upcoming videos you're going to hear from some of pwc's subject matter specialists on tools used for data and analytics and for visualizing data.