Beginner Statistics for Data Analytics - Learn the Easy Way!

Learn the difference between a population and a sample, why random sampling represents the population, and how samples enable predictions and inferential statistics.

Identify the two main data types—categorical data and numerical data—and learn how categorical data can be converted into numbers for analysis.

Explore five popular data visuals: bar charts, line graphs, pie charts, histograms, and scatter plots, and learn how histograms and scatter plots reveal data distribution and relationships.

Identify the mode as the most frequently occurring value in a data set, illustrated by 25, and use the Excel formula =mode for simple descriptive statistics alongside mean and median.

Learn to compute mean, median, and mode from online retailer December sales, interpret their differences to reveal outliers and skew, and apply these descriptive statistics with simple Excel formulas.

Dive into histograms to see data distribution and understand mean, median, and outliers; learn to create two Excel histograms of transactions, adjusting bin width to 5–8 bins, about $250 increments.

Explore how variance measures data spread around the mean, compare high and low variance examples, and learn that standard deviation is used to calculate variance.

Compute the standard deviation for sales and daily transactions in Excel, and interpret its meaning alongside the mean, median, and data distribution.

Explore the normal distribution, the bell curve where mean, median, and mode align. Understand how standard deviation defines 68/95/99 percent ranges and underpins regression.

Explore how the central limit theorem lets small samples reveal the population mean and standard deviation, with averages becoming normally distributed and enabling inferential statistics.

Explore how random samples from a population produce a near-normal distribution of sample means, illustrating the central limit theorem and enabling reliable estimates of the population mean and standard deviation.

Learn to compute confidence interval estimates from sample data using mean, standard deviation, standard error, and t-statistics from August wand sales. Then explore regression analysis for more accurate estimates.

Explore regression analysis to identify linear trends, understand correlation, and build a predictive model using a dependent variable and independent variables, visualized with scatter plots.

Explore correlation as a fixed measure between -1 and 1, and distinguish positive and negative relationships through real examples, while recognizing that correlation does not imply causation.

Interpret correlation strength with ranges like 0.7 to 1 for strong and 0.3 to 0.5 for weak, with field context, and add line of best fit to a scatter plot.

Learn how to use a trend line or line of best fit on a scatter plot to obtain a linear equation for forecasting future sales from temperature data, using Excel.

Learn the line equation y equals mx plus b to predict daily sales from outside temperature, and preview a deeper regression analysis of model accuracy and predictor strength.

Identify the three key regression statistics—multiple r (correlation), r-squared, and adjusted r-squared—and learn how r-squared measures how well the data fit the model, illustrated by temperature versus sales.

R square shows how closely data fit the trend line, and higher values indicate a better model fit; adding education, skills, and location increases it, with Excel doing the calculation.

Explore how p-values and significance F assess model reliability in regression, interpret coefficients and the intercept, and decide which variables to keep in simple or multivariable models.

Predict wand sales from gold price using a simple regression model Y = Mx + B. Interpret the 95% confidence interval with lower and upper bounds to gauge prediction uncertainty.