MS Excel: Statistics and Data Analysis

Organizations expect you to perform statistical analysis on your own. You need to crunch data as per your requirements. As you work in intense competitive work environment you don’t have a luxury to take days and days to crunch data.A strong theoretical knowledge of statistics is not suffice, you should also be able to collect, organize, visualize and analyze data with Software.

In this course you will learn how to design a research project, source data, clean data, perform data analysis with MS-Excel, interpret results, and complete research assignment in stipulated time frame.

Along with video lectures, you are provided with pdf notes and Excel work sheets to practice the learning.

In this lecture you will learn the DCOVA frame work. You are introduced to Design, Collection, Organizing, Visualizing, and Analyzing Data.

This lecture is vital for your research projects. Majority of research projects commence with an well articulated to research plan.

On completion of this lecture you will be able to differentiate descriptive stats with inferential stats.

Learn about types of Variables or Data Sets and pertinent statistical tools for each type of data set.

In this lecture you are introduced to univariate, bivariate, and mutlivariate data sets. And what types of charts and statistical techniques need to be used for each data set type is also explained.

Get familiar with measurements of scales in statistics. You will learn about - Nominal Scale, Ordinal Scale, Interval Scale and Ratio Scale.

I will also learn how and when to use each measurement scale to source the data.

In this lecture I will introduce you to Primary Data sources (experimental, observational, and survey data), and also to Secondary Data sources (Distributed data, Data from daily business activities)

Learn how to sort data using column heading and create advanced filters in MS-Excel.

Learn how to draw cluster random sample with the help of MS-Excel by using highly clustered real time large data set.

Learn how to create charts to identify trends, clusters, extreme values using MS-Excel Chart Wizard.

Drawing charts is the first step in your data presentation. With MS-Excel you can create eye catching charts and present your data in vibrant way.

You are introduced to different chart types like, column, line, bar, scatter, stock etc. and when to you which chart type.

Data Analysis Toolpak is an Excel add-in program. However, you need to load it manually. Once you load the Data Analysis Toolpak, a Data Analysis command button will appear under Data menu ribbon.

With the help of Data Analysis command button you can run many statistical tools like, histograms, correlation, regression, anova, t-test, independent t-test, moving averages, exponential smoothing etc, with writing any formulas.

Irrespective of the size of your data, you can run the analysis in few minutes.

Compute descriptive statistics methods like Mean, Median, Mode, Variance, Standard Deviation,

Skewness, and Kurtosis in few clicks with the help of MS-Excel Data Analysis command. It takes just a minute to calculate all the descriptives, irrespective of number of records or data points.

Calculate grouped frequency distribution, cumulative percentage, histogram, and normal curve chart with MS-Excel Data Analysis command.

In this lecture you will learn The Empirical Rule and Tests of Normality. Also learn how to interpret the standard deviation, and test normality of data using Skewness and Kurtosis.

The first step in understanding the relationship between two variables is to plot the data on scatter plot. In this lecture you will learn how to draw a scatter plot and fit trend line on Bivariate data set.

Correlation is used to find out relationship between Bivariate and multivariate data sets. In this lecture you will learn how to compute correlation value (s) and interpret them. Also learn various types of relationships.

Correlation and Cross-order correlations are computed with the help of MS-Excel Data Analysis command.

Regression analysis is most widely used predictive analysis technique. In this lecture you will learn how to predict or forecast using regression analysis.

Learn and compute Regression analysis to quantify the relation, direction, and strength between two variables. Regression measures the relationship between two variables i.e. Independent or Predictor variable, and Dependent or Criterion variable. With MS-Excel Data Analysis command you solve the regression equation with few click.

Multiple regression analysis is most widely used technique in predictive analytics. Multiple regression equation is an extension of simple regression equation.

Y = b0+b1X1+b2X2+b3X3+…..+e.

Multiple regression analysis allows you to fit a more sophisticated model with several variables that explain a dependent variable. You use separate casual factors, analyzing each one’s influence on what you are trying to explain. Learn and practice multiple regression analysis.

Learn how to analyze Time series data to discover a pattern or trend in the historical data and extrapolate the pattern into the future.

In this lecture you will learn how to compute Simple Moving average using MS-Excel built-in function.

Exponential smoothing is a simple and pragmatic approach to quantitative forecasting that researchers found useful due to its short-term precision and cost effectiveness. In exponential smoothing the forecast is constructed from an exponentially weighted average of historical data.

Learn how to Calculate exponential smoothing is with Excel Data Analysis command.

Learn how to test the hypothesis of similarity and dissimilarity between two paired samples. You are also introduced to frame null and alternative hypothesis.

Context, assumptions, and interpreting the test results are also explained.

The independent samples t-test evaluates the difference between the means of two independent or unrelated groups. The t-test evaluates whether the mean value of the test variable for one group differs significantly from the mean value of the test variable for second group.