Application of Statistics in Business Analytics

Explore numerical (discrete and continuous) and categorical (nominal and ordinal) data types, learn to define and convert data for analysis, and note binary data and dummy variable traps.

Explore central tendencies to summarize large datasets, learning mean, median, and mode, their advantages, challenges with outliers, and data ordering for accurate representation.

Explore how mean, median, and mode behave in symmetric and skewed distributions, and learn when to use each central tendency in real-world data such as income and housing prices.

Explore the concept of variability and learn how measures of variability such as range and standard deviation describe data dispersion for business analytics.

Learn how to compute the range as the difference between the maximum and minimum values, and understand its use and limitations for measuring data variability.

Explore standard deviation and variability by comparing observations to the mean, derive mean deviation and variance, and apply empirical rules within normal distribution.

Compute the interquartile range by ordering data and identifying Q1 and Q3. Highlight variability across quartiles and the median, and show robustness to outliers and handling skewness.

Explore measures of variability in Excel by computing the range from max and min values, then calculate standard deviation and the interquartile range from quartile 1 and quartile 3.

Learn discrete and continuous uniform distributions, differentiate ideal and practical scenarios, and apply area under the curve and distribution functions to compute probabilities, means, and variance.

Explore the exponential distribution as a model of time between events, highlighting its positive skew and that the mean and standard deviation both equal 1/λ.

Explore the normal distribution, also called the glosson distribution, a bell-shaped, symmetric curve with mean, median, and mode aligned, and learn how standard deviation governs data variability and 68–95–99% rules.

Explore inferential statistics and why we need them, using sample data to represent a population. Learn how random sampling enables extrapolation, inferences, and practical analysis despite errors and outliers.

Explore sampling to infer population characteristics by selecting equal-sized samples, covering simple random, systematic, stratified, cluster, and biased methods, and examining sample size and representativeness for inference.

Explore inferential statistics and estimation statistics by estimating population parameters from sample data, using point and interval estimates to gauge uncertainty in advertising expenditure decisions.

Apply interval estimates and confidence intervals in business analytics by using sample data, standard error, and the central limit theorem to bound population parameters.

Learn ordinary least squares to build linear regression models that predict future values from past trends, using the line y equals b0 plus b1 x, and check linearity for analytics.

Analyze the simple linear regression output, focusing on regression statistics, ANOVA, and model statistics to assess accuracy and refine predictions.

Explore multiple linear regression as a multivariate technique with two or more independent variables, using least squares, interpreting coefficients, and applying diagnostics like adjusted r-squared.