Statistics for Data Analysis Using Excel

Learn to use Excel functions to compute statistics such as sum, sum of squares, and sum of products using simple and array formulas.

Explore Excel's statistical functions to compute average, variance, standard deviation, correlation coefficient, and standardized scores from a data set, using formulas and anchored references for accurate results.

Explore Excel graphics for statistical work by creating column charts, labeling axes, adding data labels, and transforming charts—from bar graphs to scatter plots—using practical steps.

Install the Excel analysis toolpak on Windows, access data analysis tools, and run descriptive statistics on rainfall data with labels in the first row and results in a new worksheet.

Differentiate nominal, ordinal, interval, and ratio data types and explain how their scales shape identity, magnitude, intervals, and a meaningful zero point.

Explore constants and variables in statistics, focusing on independent variables that researchers manipulate and dependent variables they measure, with the x axis for independent and the y axis for dependent.

Explore probability as a tool to quantify uncertainty in decisions by defining experiments, trials, elementary outcomes, events, and sample spaces, using dice and coin examples.

Explore probability basics with sample space, elementary outcomes, and simple probability using dice. Learn to compute unions, intersections, and independent events with practical examples.

Explore conditional probability by applying P(A|B) = P(A∩B)/P(B), with examples such as the probability of rolling a four given an even number, and its use in jury decision making.

Learn how the mean, or average, measures central tendency as the sum divided by n, and how adding a constant changes the mean, with Excel examples.

Discover how the median serves as a measure of central tendency, how to compute it in Excel, and why it stays stable with outliers, unlike the mean.

Learn how to identify the mode—the most frequently occurring number—using Excel's mode functions, including mode.single and mode.mult, and handle bimodal distributions with an array formula.

Explore variance as a measure of variability, using deviations and squared deviations to compute mean squares; distinguish population versus sample variance and illustrate Excel variance formulas.

Demystify variance and standard deviation, distinguish population and sample calculations, and learn formulas to express variability in the same units as data.

Calculate z-scores by subtracting the mean and dividing by the standard deviation to place scores in context, then compare distributions across groups.

Organize a distribution of January temperatures from 1972–2015, compute four-year interval averages, and visualize the results with line charts and frequency distributions in Excel, including cumulative frequencies.

Create frequency polygons by plotting interval midpoints against frequencies in Excel, using a scatterplot with markers and lines to compare January and March temperatures.

Explore properties of a distribution using Excel to compute mean, median, and modes, and distinguish population and sample variance and standard deviation with real temperature data.

Explore probability distributions by distinguishing discrete and continuous cases. Compute the mean, variance, and standard deviation for outcomes using coin tosses and dice, with probability mass and density function concepts.

Meet the standard normal distribution, convert scores to z scores with mean zero and standard deviation one, and read P(1<Z<2)=0.1359 from its equation.

Learn to convert any normal distribution to z scores, compute probabilities in the standard normal distribution with an IQ example, and use Excel's NORM.DIST to find areas between z values.

Explore the normal distributions using Excel functions and graphing. Create x values, compute probability densities, and plot a smooth normal curve; adjust mean and standard deviation to see effects.

Discover sampling distributions, including the mean across many samples and the standard error, to estimate parameters and test hypotheses from sample data for data analysis using Excel.

Understand the central limit theorem and the sampling distribution of the mean, which becomes normal with n of 30 or more, enabling estimation and hypothesis testing with the standard error.

Examine how the t distribution adapts inferential statistics when population parameters are unknown, using x-bar and s to estimate mu and sigma, with degrees of freedom shaping its tails.

Estimate the population mean with X bar, quantify uncertainty with alpha, and interpret 95% confidence limits as two-tailed bounds from the sampling distribution.

Calculate confidence intervals from the sample mean, sample standard deviation, alpha, and sample size using the standard normal distribution or the t distribution.