Statistical Analysis and Research using Excel

Explore the base of statistics, covering data types, population versus sample data, nominal to ratio measures, and the statistical analysis process for business decision making.

Configure Excel for research and statistical analysis, learn to use formulas and functions, access the data analysis tools from the data tab, and enable analysis options for diverse statistics.

Explore basic aggregations in Excel, including sum, sumif, count, countif, min, and max, and learn how to apply them to ranges with criteria.

Apply basic Excel aggregations to sales data by using sum and sumif for totals and conditional totals, count and countif for observations, and min and max to identify extremes.

Explore data visualization fundamentals by distinguishing dimensions and measures and applying bar, line, pie, area, donut charts, and scatter charts, plus histograms for statistical analysis.

Analyze central tendency by covering average values, partition values, and repeated values. Discover arithmetic, geometric, and harmonic means alongside median, quartiles, deciles, percentiles, and the mode.

Compute the arithmetic mean as the sum of observations divided by their count, for ungrouped and group data, using sigma x, FiXi, and Excel's average function.

Compute arithmetic mean and use count, countif, and averageif on a batsmen dataset to compare runs and matches, identifying top performers like David Warner and Virat Kohli.

Explore the geometric mean, the nth root of the product of observations, and apply its ungrouped and grouped data formulas with Excel's geomean function to real-life data such as CAGR.

learn how to compute geometric mean in excel using a dataset of top hundred deliveries across years, compare yearly and bowler-specific averages, and interpret the results.

Understand the harmonic mean, a reciprocal-based average, with formulas for ungrouped and group data and the Excel function, and its use for speeds and ratio-scale data.

Learn to compute the harmonic mean in Excel using the IPL bowler dataset to compare economy rates, clean zero values, and contrast with arithmetic mean for ratio data.

Explore the median concept as the middle value that partitions data into two equal parts. Learn its calculation for odd and even datasets and apply Excel's median function to examples.

Apply the median function in Excel to an IPL dataset and compare batsmen by 50 percent of innings. Use conditional formatting color scales and preview quartiles, percentiles, and deciles.

Understand the quartile concept, dividing data into four equal parts, with Q1, Q2 (median), and Q3, and apply Excel quartile functions with inclusive and exclusive median options in business data.

In this lab, learn to compute quartile one and quartile three in Excel to compare batsmen performance, using median, top 25 percent innings, and conditional formatting to highlight leaders.

Explore the percentile concept, its mathematical basis, and how to compute it in Excel using the inclusive and exclusive variants, with examples for business applications.

Analyze percentile analysis in excel on an IPL batsman dataset by computing the 20th and 80th percentiles and comparing worst and best performances with conditional formatting.

Learn the decile concept, its n+1 times i divided by 10 calculation, and how to estimate deciles in Excel using percentile functions with practical business examples.

Learn how to compute deciles, the 90th percentile, and other measures in Excel to compare the top ten percent performances of seven batsmen using the IBL Bettmann dataset.

Master the mode, the most frequent value, by using the three times median minus two times the mean formula and Excel to find the most common ratings.

Analyze amazon reviews in excel by calculating the mode to identify the most frequent rating, then compare it with the average to better understand customer satisfaction.

Explore measures of dispersion in data, comparing absolute and relative measures and applying range, mean deviation, standard deviation, and quartile deviation to assess variability.

Explore range as a measure of dispersion, defined by the largest minus smallest observation, and learn to compute range and range coefficient in Excel using min and max functions.

Explore calculating rainfall range and coefficient of range in Excel using India's subdivisions data from 1901 to 2017, comparing variability across regions.

Learn how mean deviation measures the average absolute difference from a central tendency, using mean or median, and compute it manually or in Excel.

Calculate mean deviation and coefficient of mean deviation in an Indian rainfall dataset using Excel, then compare regional variability across subdivisions from 1990 to 2017.

Explore quartile deviation, based on the first and third quartiles, stable under sampling fluctuations and origin changes but affected by scale; calculate as (Q3−Q1)/2 and the coefficient as ((Q3−Q1)/(Q3+Q1))*100.

Analyze a lab using Indian cricket ODI data from 2008 to 2017 to compute first and third quartiles and coefficient of quartile deviation for runs and balls faced in Excel.

Explore standard deviation as a dispersion measure, covering its formula for sample and population data, its ties to normal distributions, and manual and Excel calculations.

Learn to compute standard deviation (sample) and coefficient of variation in Excel for eight batsmen from 2008–2017, using mean and color coding to identify the most consistent performer.

Explore variance, the square of deviation, its formulas for sample and population data, and how to calculate it in Excel with examples and standard deviation and coefficient of variation.

Calculate population and sample variance in excel to compare rainfall variability across Indian subdivisions and identify regions with highest and lowest variance.

Explore correlation concepts and its six types—perfect positive, perfect negative, partial positive, partial negative, no correlation, and curvilinear—plus calculation methods like scatter diagrams, Pearson's r, and Spearman's rho.

Demonstrates how to assess correlation using scatter diagrams for linear and non-linear relationships. Shows how to plot in Excel, interpret patterns, and spot outliers in business data.

Explore correlation using scatter diagrams on India commodity prices (2012–2017) with multiple commodities. Identify partial or negative correlations and note that scatterplots do not provide exact correlation values.

Learn how Culberson's correlation coefficient measures linear relationships using covariance over standard deviations. Compute it in Excel and apply it to stock and commodity price relationships.

Explore how to compute Pearson correlation in Excel across stock volume and daily returns for three Indian stocks, using data analysis tool, and color scales to compare variable pairs.

Learn rank correlation as an easy, ordinal-variable measure of agreement and relationships, using Excel to rank data, compute differences, and apply to judges, sales, and tests.

Explore rank correlation in Excel using a Bollywood movie ratings dataset from Times of India, MTV, and IMDB, and learn to compute ranks and interpret correlations.

Learn how regression predicts a dependent variable from independent variables using simple linear, multiple, logistic, and stepwise methods, with intercept, slope, residuals, standard error, and R-squared.

Explore linear regression analysis and how to derive the regression line using slope and intercept formulas, compute values by hand, and apply regressions in Excel for business predictions.

In this lab, perform regression analysis in Excel to predict Nifty from three stocks' closing prices and volumes, using alpha and beta, and assess R-squared and correlation.

Analyze residuals to evaluate the regression model by comparing actual and predicted values, and compute standardized residuals and the standard error of estimate in Excel.

Use residual analysis in Excel to validate a regression model predicting Indian stock prices from closing prices and volumes. Learn to examine standardized residuals, identify outliers, and assess model fit.

Explore multiple regression, an extension of simple linear regression using two or more independent variables to explain a dependent variable, with its math form and Excel usage.

Explore multiple regression analysis of India's rainfall in Excel, using India's rainfall as dependent variable and subdivision rainfall as independent variables, and evaluate model fit with R-squared and standard residuals.

Explore the concept of research, and distinguish basic from applied studies in business contexts. Learn how data collection, compilation, analysis, and interpretation drive decision making and the continuous research cycle.

Explore the methods and process of research in business, including exploratory, descriptive, and causal research, and learn the end-to-end business research process from problem identification to data analysis and implementation.

Compare primary and secondary data, exploring how researchers collect data through surveys, observations, and experiments, and evaluate sources from internal databases to government and media resources.

Learn how to formulate and test a hypothesis, including null and alternate hypotheses, interpret results with calculated and critical values, and understand type one error and type two error.

Learn the basics of data collection, explore primary and secondary sources, surveys, observations, and other methods, and understand data quality traits: adequate, valid, and measurable.

Design a questionnaire by defining the research objective, considering respondent characteristics, selecting formats, and sequencing questions—open-ended, dichotomous, and multiple-choice.

Analyze levels of measurement from nominal to ratio and learn how to choose single item, multiple item, and continuous scales, guided by validity, reliability, and sensitivity.

Understand sampling as a tool to gather population data and design representative samples using simple random, stratified, cluster, systematic, and multi-stage methods, plus non random sampling, and common sampling errors.

Code questionnaire responses into numeric values, then enter, edit, and clean data, addressing missing data, format mismatches, and merging multi-source datasets with metadata.

Describe dataset attributes using descriptive statistics, summarizing data with central tendency and dispersion. Use these insights in reports to stakeholders and in research to form hypotheses.

Explore descriptive statistics in Excel, including basic aggregations (min, max, sum, count), central tendency (mean, median, mode), dispersion (range, standard deviation, variance), and skewness and kurtosis.

Explore descriptive statistics in Excel using the India rainfall dataset across seven subdivisions, computing mean, median, and standard deviation, then assemble a clean, comparative table.