Business Statistics Using Excel

Construct a line graph in Excel using sales turnover data from 2004 to 2015, including selecting data, inserting the chart, and naming axes.

Explore constructing simple bar graphs for a single variable and multiple bar graphs for comparing two or more variables in Excel, with step-by-step chart labeling of axes and titles.

Learn how to create a simple bar graph in Excel by selecting data, inserting a chart, adding axis titles and a chart title, and formatting colors.

Demonstrates constructing a subdivided bar graph in Excel to compare total capital formation by private and public sectors, including data selection and chart layout steps.

Explore how scatter diagrams reveal relationships between two variables and aid prediction, using money supply and inflation as an example. Also learn to construct scatterplots in Excel.

Learn to use Excel's data analysis to compute descriptive statistics, construct a pie chart, and calculate percentage shares of plan components.

Explore the fundamental rules of counting, including the counting principle, factorials, and the differences between permutations and combinations, with practical examples and an eye toward probability theory.

Explore the multiplication rules of probability, including independent and dependent events, and apply joint, marginal, and conditional probabilities using cricket and hockey examples.

Explore binomial distribution as a discrete binomial experiment with fixed trials, independent outcomes, and constant probability of success, and learn to compute exact numbers of successes using the binomial formula.

Compute probabilities under the normal distribution by standardizing x to z, using the z-table and symmetry, with mu=78 and sigma=15 for p(x<50) and p(80<x<90).

Learn how to draw inferences about a population from a representative sample, define population and sample, compare population and sample means and variances, and understand census versus sampling.

Learn about sampling types, especially simple random sampling and equal chance via random numbers; compare stratified, systematic, and cluster and non-probability sampling, and note small versus large population effects.

Explore systematic random sampling and other probability sampling methods. Learn stratified and cluster sampling, use random starting points, and apply simple random selection across population strata.

Explore the difference between an estimator and an estimate in inferential statistics, and see how sample statistics estimate population parameters through statistical estimation.

Learn to construct confidence intervals for large and small samples using standard normal and t-distributions, with 90, 95, and 99 percent levels, and apply to means and proportions.

Learn the procedure of hypothesis testing: define null and alternative hypotheses, set a significance level, compute a test statistic, and decide whether to reject the null.

Explore one-sample hypothesis testing for a population proportion, formulating a null hypothesis, selecting a 5% level, and testing whether the proportion is below 20 percent.

Test population variance with a chi-square test using sample variance, degrees of freedom n-1, and a 5% significance level to determine if variance differs from a specified value.

Learn how to perform an F test to compare two population variances using Excel, with finance examples illustrating variance as a measure of risk and higher variance implies higher risk.

Learn how to perform an F-test in Excel to compare two sample variances, test the null hypothesis of equal population variances using stock price data and observations.

Use the chi-square goodness of fit test to compare observed and expected frequencies; under a large-sample assumption, the example shows chi-square exceeding the 5% critical value, rejecting uniform monthly sales.

Assess one-way ANOVA by decomposing total variation into variation explained by a categorical independent variable and error, then test equal group means with the F statistic, illustrated with three branches.