Business Statistics Using Excel

Explore introductory descriptive statistics, describing data through graphs, symbols, and formulas. Learn how to handle data from populations and summarize numerical information for meaningful conclusions.

Learn how to summarize data using graphical methods, emphasizing diagrams versus graphs, and the steps to construct axes, choose scales, and create meaningful titles on graph paper.

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.

Learn to create a multiple barograph in Excel, comparing total exports and imports, with customization of colors, axis labels, and a chart title.

Learn to create multiple bar graphs and subdivided component bar graphs and percent bar graphs in Excel, including data setup, axis labeling, and chart titles.

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.

Learn to create a percentage bar graph in Excel, displaying tax revenue, non-tax revenue, and capital service as shares of the total, with 100 percent height and labeled axes.

Learn how pie charts visualize parts of a whole, determine proportions for 360 degrees, and create them manually or in Excel using advertising expenditure and sector data.

Learn to construct a pie chart in excel to visualize planned outlays, calculate percentages like education’s share, and customize labels, colors, and titles for clear business statistics visuals.

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 how to construct a frequency distribution from raw data by grouping into class intervals, determining the number of classes, and interpreting relative frequencies.

Explore summarizing data with numerical methods and descriptive terms, highlighting when precise numerical values are preferable and noting skewness as a key technique.

Explore mean, median, and mode as core measures of central tendency, and learn how to compute them. Understand how outliers affect the mean and why median suits skewed data.

Explore measures of dispersion in business statistics using Excel, including range, mean absolute deviation, standard deviation, and coefficient of variation, to assess data spread around central tendency.

Explore measures of shape, including skewness and kurtosis, to judge normality in data using Excel. Identify how skewness indicates symmetry and how kurtosis reveals distribution height, guiding central tendency decisions.

Demonstrate calculating measures of central tendency and dispersion in Excel using descriptive statistics, including standard deviation, skewness, and normal distribution assessment across sample data.

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

Explore the concept of probability as a numerical measure of likelihood, with zero-to-one values, where 0.5 indicates a 50–50 chance for uncertain events in daily life and business.

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 probability concepts by defining random experiments, outcomes, and sample spaces, and classify events as mutually exclusive, equally likely, or collectively exhaustive.

Explore basic set concepts, including universal sets, complements, and finite collections of elements, and learn how Venn diagrams illustrate intersection, union, and sets with no common elements.

Discover the addition rule in probability for mutually exclusive and non mutually exclusive events, learn how to compute probability of A union B using sums and intersections.

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

Apply the multiplication rule for dependent events to compute the joint probability using conditional probability, and explain marginal and conditional probabilities with a class-based example.

Explore the law of total probability and its derivation of Bayes' theorem by conditioning on partitions of the sample space and expressing total probability as the sum of joint probabilities.

Apply Bayes' theorem using the law of total probability and conditional probability to a three-machine defect example, computing the overall defect probability and the posterior probability of each machine.

Define a random variable as a numerical outcome of an experiment, and examine discrete and continuous types with their probability distributions, including mathematical expectation and variance.

Contrast discrete random variables, which take only specific numerical values, with continuous random variables that can assume any value within an interval, such as height or distance.

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.

Learn to compute the expected value and variance of a binomial distribution using n and p, where mean is n p and variance is n p (1-p).

Learn to compute binomial probabilities in Excel with the binomial distribution, obtaining exact and cumulative probabilities for given trials and a chosen probability of success.

Explore the Poisson distribution as a discrete probability distribution for counting occurrences in a fixed interval, with independence, and learn to compute probabilities using Excel.

Explore the normal distribution, a continuous model, as the basis for inferential statistics, defined by mean and standard deviation. Learn to use z-scores to standardize values and assess probabilities.

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).

Compute normal distribution probabilities with Excel by using the mean and standard deviation to find P(X<...), P(X>...), and P(a<X<b) for lifespans such as CFL bulbs.

Learn population, sample, and census concepts and how sampling distribution of means and proportions reveals population characteristics through samples.

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.

Explore population versus sample, and how parameters (mu, sigma) and statistics (x-bar, s) describe characteristics while using sampling distribution and inferential statistics to estimate population features.

Learn the basic theorems of random sampling and the sampling distribution of the sample mean, including its standard error and the central limit theorem.

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.

this lecture explains non-probability sampling and its forms, including judgment-based and convenience sampling, contrasting them with probability sampling and highlighting biases and representativeness concerns.

Determine the appropriate sample size by aligning confidence level and margin of error, using standard deviation and sampling error; for 95% confidence and 5% margin, about 171 respondents are needed.

Explore how to estimate population characteristics from sampling data, and draw inferences using representative samples rather than a full census.

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

Identify the qualities of a good estimator—unbiasedness, consistency, efficiency, and sufficiency— and see how the sample mean serves as an unbiased estimator of the population mean.

Explore point estimation as a single value for a population parameter and interval estimation as a range with a confidence level, illustrating with a company's sales example.

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.

Master hypothesis testing concepts, procedures, and significance levels in Excel, covering type I error, power, and tests for means and proportions with large and small samples, chi-square and F-tests.

Formulate null and alternate hypotheses and gather evidence from a random sample to test them. Manage Type I and II errors through significance levels and sample size.

Explore two-tailed hypothesis testing with the normal distribution, testing a null mean against an alternative not equal to it, and focusing on the symmetric tails of the distribution.

Define a one-tail test using a null hypothesis that the population mean is at most zero and an alternative that it is greater than zero, focusing on the right tail.

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.

Learn to compare two independent population means using large-sample hypothesis testing, formulating H0: mu1 = mu2, computing the test statistic and critical value at 5% significance with sample data.

Perform a hypothesis test of the population mean for small samples in Excel. Work with two independent samples of equal variances, set a significance level, and decide if means differ.

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.

learn to perform a two-sample t-test assuming equal variances in excel, compare independent samples, state null hypothesis, choose alpha, and interpret t and critical values to conclude if means differ.

Learn to perform a hypothesis test for a population mean with unequal variances using independent samples, estimate population variance from sample variances, and compute degrees of freedom at 1% significance.

This lecture demonstrates a two-sample mean hypothesis test for independent samples in Excel, computing the test statistic and critical value to decide whether to reject the null hypothesis.

Explore hypothesis testing for two independent samples with unequal variances in Excel, testing whether population means differ, and interpreting critical values.

This lecture uses a paired t-test for dependent samples to compare growth before and after liberalization, showing how to state hypotheses, compute statistic, and conclude no significant difference at 5%.

execute a paired t-test in Excel by selecting the data analysis toolpak, inputting variable one and two, setting the hypothesized difference, and interpreting the null hypothesis of no mean difference.

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.

Explore the chi-square test for categorical variables, comparing observed versus expected frequencies to assess goodness of fit, and understand degrees of freedom and its relation to binomial and normal distributions.

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.

Apply the chi-square test of independence to examine relationships between two categorical variables using contingency tables and expected frequencies. The example shows voting is independent of sex at 1% significance.

Explore analysis of variance (ANOVA) to test whether means across two or more populations are equal, using a quantitative dependent variable and categorical independent factors.

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.

Learn to perform a one-way ANOVA in Excel using data analysis and group by options; interpret the F statistic and critical values to determine if means differ.

Explore two-way ANOVA without replication by decomposing total variation into factors X1, X2 and interaction, and assess significance with F-tests using an example from student satisfaction across courses and centers.

perform a two-way ANOVA without replication in Excel, test mean differences at 5% significance, compare calculated and critical F, and interpret null hypotheses across MBA and marketing courses.

Perform a two-way ANOVA with replication in Excel to test course and center effects at 5% significance, revealing no interaction and no significant main effects in the sample.