Microsoft Excel: Essential Statistics for Data Analysis

Explore why statistics matter for business intelligence and decision making. Identify populations and samples, learn the statistics workflow, and recognize when statistics are or aren’t needed.

Explore why statistics helps business intelligence decisions under uncertainty, and when to rely on direct data analysis with Excel or BI tools instead of complex tests.

Identify populations and samples, recognize parameters and statistics, and use sample statistics to estimate population parameters, guiding decisions under uncertainty.

Apply a statistics workflow in Excel to move from descriptive statistics to population modeling using distributions, central limit theorem, confidence intervals, hypothesis tests, and regression analysis.

Explore descriptive statistics in Microsoft Excel to understand data, including frequency distributions, central tendency, variability, and measures such as mean, median, mode, and standard deviation, histograms, and box plots.

Explore descriptive statistics to summarize a variable, turning large data sets into a few figures such as mean, min, max, and a histogram to show the grade distribution.

Identify and distinguish numerical and categorical variables in a dataset, recognizing when to aggregate or filter, with examples like student identifiers and mean salary by work experience.

Explore distribution, central tendency, and variability in descriptive statistics, using frequency tables and histograms, mean, median, mode, and measures of spread like range, quartiles, and standard deviation.

Explore frequency distributions by building pivot tables or countifs function, compute relative frequencies and percentages, and use unique values and dynamic array formulas to analyze status and degree data.

Explore numerical frequency distributions by grouping data into bins, building grouped frequency tables and histograms, and calculating cumulative relative frequency in Excel using pivot tables or the frequency function.

Explore histograms as a fast visualization of a numerical distribution, using grouped frequency tables and Excel column charts to compare frequencies, adjust bin widths, and reveal central tendency and variability.

Learn to create a frequency table and visualize it with a histogram using the Excel statistics workbook, as you tackle the first assignment on frequency distributions.

Use a pivot table in Excel to create a frequency table for MBA grades, group scores into five-point bins from 60 to 100, and build a histogram to show distribution.

Explore measures of central tendency: mean, median, and mode—and how outliers distort the mean, with hands-on Excel techniques using the average function, median, and mode, plus average ifs.

Explore how skew measures distribution asymmetry around mean, with right skew raising mean above median and left skew lowering mean. Note that zero skew aligns with normal distribution.

Explore measures of central tendency by calculating the mean, median, mode, and skew of MBA grades by undergrad degree, comparing business undergrads with other undergrads.

Calculate and compare the mean, median, mode, and skew of MBA grades for business undergrads and other undergrads in Excel, then interpret the distribution differences.

Calculate the range to measure variability with min and max values. Note that a single outlier can skew the spread, as shown with before and after employability scores in Excel.

Explore the interquartile range by identifying the first and third quartiles, median, and the five-number summary; calculate with Excel using inclusive and exclusive quartile methods and dynamic arrays.

Explore how box and whisker plots visualize the five-number summary, quartiles, median, and outliers to reveal distribution, central tendency, and variability, with hands-on Excel steps for comparing groups.

Explore how standard deviation measures how far values lie from the mean, and how higher values indicate wider distributions, while variance shows its squared scale, with Excel examples.

Compute the coefficient of variation by dividing the standard deviation by the mean to compare variability across variables on different scales, using Excel for practice.

Calculate the range, interquartile range, and standard deviation for MBA grades by undergrad degree, compare these groups with a box plot, and interpret dispersion.

Explore how to compute range, interquartile range, and standard deviation for MBA grades by undergrad degree, using Excel formulas and a box plot to compare dispersion and skew.

Identify the two main types of variables: numerical and categorical. Visualize distributions with histograms, compare mean and median, and use standard deviation with a box plot for context.

Practice descriptive statistics on Maven Pizza Parlor daily sales to inform demand planning, using the Maven Business School data set with date and weekday.

Learn to summarize daily pizza sales with descriptive statistics, including mean, median, mode, standard deviation, and the five-number summary, plus how weekday patterns and histograms inform sales.

Explore the basics and types of probability distributions, including the normal distribution's properties and its relationship with frequency distributions, and use Excel to calculate probabilities, values, and Z scores.

Discover probability distribution as an idealized frequency distribution of possible values and their probabilities, illustrated with the sums from two dice and a binomial model of the population.

Explore discrete and continuous probability distributions, including uniform, binomial, Poisson, exponential, and normal, and learn how probabilities are represented by bars or areas, with emphasis on the normal distribution's applications.

Explore the normal distribution, a symmetric bell curve described by mean and standard deviation, and use Excel to compute probabilities and compare mean and median.

Compute z scores by subtracting the mean and dividing by the standard deviation, then interpret extreme values using the standard normal distribution, with the Excel standardized function as an option.

Apply the empirical rule to a normal distribution, using 68%, 95%, and 99.7% intervals of one, two, and three standard deviations to interpret z-scores and MBA grades in Excel.

Analyze graduate salary distribution to assess normality by plotting the bell curve, checking mean equals median, and calculating the share within one to three standard deviations per empirical rule.

Visualize annual salary data with a histogram, compare mean and median, and apply the empirical rule using a 45,000 standard deviation to assess normality, concluding data are not normally distributed.

Explore Excel's normal distribution functions as a cheat sheet, including norm dist, norm env or inverse, standardized function for z scores, and norm s dist and norm s in.

Use Excel functions to calculate probabilities for a normal distribution, including cumulative probability and between x1 and x2, while noting exact values have zero probability.

Explore how the norm.dist function calculates cumulative probabilities and density for a normal distribution, using x, mean, and standard deviation, with true or false options.

Learn to use the norm.dist function for the standard normal distribution, returning the area under the curve (true) or the curve height (false), with z-score examples like -1.5.

Apply the norm.dist function to compute the probability of a grade of 90 or higher under a normal distribution, ensuring the honor-student threshold stays under 10%.

Calculate the probability of an MBA grade of 90 or higher with a normal distribution mean 80.17 and standard deviation 6.17, using norm.dist and subtracting from 1 to get 5.56%.

Plot the normal curve in Excel using a line and area chart for MBA grades, mean 80.17 and sd 6.17, and display the shaded area with an if function.

Estimate x or z values from the normal distribution given a cumulative probability. Use percentiles to craft values and build confidence intervals.

Apply the NORM.INV function to derive x values from a normal distribution using a cumulative probability, mean, and standard deviation.

Explore how the norm.s.inv function computes z scores in the standard normal distribution using cumulative probabilities, with examples for the top 5% and bottom 20%.

Calculate the top 10% cutoff for a normal population using the norm in function, and its z-score with the norm S10 function, given a 90 threshold for honor students.

Learn to compute the top 10% cutoff and the corresponding z-score using Excel's normal distribution functions, yielding an 88.1 grade threshold and a 1.28 standard deviation above the mean.

Discover how probability distributions serve as idealized frequency distributions, explain the normal distribution’s symmetry, and use Excel’s norm dist, norm assist, and norm estimate functions for interval probabilities.

Analyze Maven Medical Center birth weights to test normality and compute the probability of weights ≤ 2.5 kg, then estimate 1% and 99% thresholds and count births below and above.

Analyze birth weights data using histograms and normal distribution assumptions; compare mean and median, apply the empirical rule, compute standard deviation, and calculate probabilities with Excel's norm.dist function.

Master the central limit theorem and its link to the normal distribution, and learn how standard error, sampling distributions, and sample size affect normality and variability.

See how the sampling distribution of the mean, illustrated with Airbnb price data, becomes normally distributed around the population mean as sample size grows, illustrating the central limit theorem.

Demonstrate the central limit theorem by simulating a population in Excel, drawing random samples, and showing the sampling distribution of the mean centers at the population mean.

Compute the standard error as the population standard deviation divided by the square root of the sample size, illustrating the sampling distribution of the mean with Airbnb prices.

Explore the four implications of the central limit theorem for inferential statistics, including using population mean and standard deviation to build the sampling distribution and assess sample membership.

Apply the central limit theorem to build confidence intervals and conduct hypothesis tests using sample means and standard deviations to draw conclusions about population mean with Excel normal distribution functions.

Sample means cluster around the population mean and become more normal as sample size grows, with at least 30 for the central limit theorem to apply, and standard error decreases.

Learn to estimate population parameters with confidence intervals using sample statistics, determine the margin of error, and compare means and proportions between two populations using the RT distribution when unknown.

Learn how to construct a confidence interval from a sample, using a point estimate like the sample mean, margin of error, and confidence level to infer the population mean.

Explore how alpha values of 0.10, 0.05, and 0.01 set confidence levels (90%, 95%, 99%), and why higher confidence widens the interval while you should choose an accepted error probability.

Learn to calculate the margin of error and build a 95% confidence interval around the sample mean using the standard error and the z-score, with formulas and practical example.

Calculate a 90% confidence interval for graduates' employability score using the population standard deviation, sample size, and mean. The interval indicates 274 to 304 on average.

Compute the margin of error with the confidence.norm function in Excel, using alpha, population standard deviation, and sample size to produce the same confidence interval as the manual method.

Compute the sample mean and size from MBA graduate salaries, set a confidence level, and determine the margin of error and confidence interval limits using the known population standard deviation.

Estimate the mean annual salary of graduates with a confidence interval in excel, using a sample and population standard deviation, to get 106,008 to 132,000 at 80% confidence.

Explore z and t confidence intervals, with known versus unknown population standard deviation. See how standard error uses the sample standard deviation and how the t distribution sets critical values.

Explore the t distribution, a fat-tailed alternative to the standard normal, shaped by degrees of freedom; see how small samples widen tails and higher degrees closely resemble the z distribution.

Explore Excel's t distribution functions to compute cumulative probability or density, obtain critical values for confidence intervals, and derive margins of error using alpha, standard error, and degrees of freedom.

Calculate confidence intervals with the t distribution when the population standard deviation is unknown, using sample standard deviation, standard error, degrees of freedom, and t critical values.

Apply confidence intervals with the t distribution using a sample of graduates to calculate standard deviation, set a confidence level, compute the margin of error, and determine the interval limits.

Construct an 80% confidence interval using the t distribution from a 53-sample study to estimate the mean annual salary, yielding 111,000 to 127,000 and higher than the national mean.

Learn to compute confidence intervals for proportions from a sample using p hat, the z distribution, and the standard error, with the central limit theorem check.

Calculate the sample proportion and verify the central limit theorem for placement data. Set the 95% confidence interval by determining the margin of error for the population proportion.

Apply Excel to compute a 95% confidence interval for graduates placed by two months after graduation, using sample size, sample proportion, central limit theorem checks, and the z value.

Compare the difference between two population means or proportions using confidence intervals, distinguishing dependent (paired) samples from independent samples, and assume unknown sigma, the most common real-life scenario.

Calculate confidence intervals for dependent samples by differencing paired observations, then apply the t distribution to estimate the mean difference between MBA and undergrad grades.

Explore confidence intervals for dependent samples by analyzing employability scores and a 50-point improvement, calculating the difference, mean, standard deviation, margin of error, and 90% interval limits.

Compute a 90% confidence interval for the mean difference in employability scores before and after the MBA program, using dependent samples in Excel to show the program's impact.

Compute confidence intervals for independent samples with a two-sample t framework, using standard error from variances and sample sizes. The example yields a 90% CI [-0.4, 4.1] for mean difference.

Calculate the means and variances of two independent samples, determine the difference in means and the margin of error, and construct a 90% confidence interval for employability scores.

Compute a 90% confidence interval for the difference in population mean employability scores between placed and not placed graduates using Excel, with sample statistics and margins of error.

Compute the confidence interval for the difference between population proportions using a point estimate, margin of error, standard error, and the z distribution, with a practical Excel example.

Explain using samples to estimate population values with confidence intervals, guided by confidence level, critical value, and standard error; use the RT distribution when sigma is unknown.

Lead statistician at Maven Pharma analyzes trial data to compare treatment and placebo groups using a 99% confidence interval for population proportions, assessing central limit theorem applicability and drawing conclusions.

Compute a 99% confidence interval for the difference in proportions of patients with marked improvement between treated and placebo groups, using independent samples and the standard error of the difference.

Assess population parameters using hypothesis tests by framing null and alternative hypotheses, evaluating mean and proportion tests, and understanding Type I and Type II errors for one and two-sample comparisons.

Learn how a hypothesis test evaluates whether a sample supports a population assumption, guiding decisions under uncertainty through steps from state null and alternative to reject or fail to reject.

Define null and alternative hypotheses and frame the test around the population mean, using the 355 milliliter example and the status quo as the baseline.

Set the significance level to judge when evidence rejects the null hypothesis. See how alpha marks the rejection region around the hypothesized mean using a soda-filling example.

Compute the t-score by taking the sample mean minus the hypothesized mean, divided by the standard error, and interpret its distance from the hypothesized sampling distribution.

Explore the p value, its meaning under the null hypothesis, and how to compute it from a sampling distribution in Excel using sample mean, a test statistic, and significance level.

Learn how to draw conclusions from hypothesis tests by comparing p-values to alpha and deciding when to reject or fail to reject the null hypothesis, clarifying implications for population parameter.

Apply a hypothesis test to determine if the curriculum yields an average of 80, using null and alternative hypotheses, significance level, test statistic, and p-value to draw a conclusion.

Frame null and alternative hypotheses for a one-sample test with mu 80. Compute the standard error, test statistic, and p-value, then compare to alpha 0.2 to decide about the curriculum.

Explain the link between confidence intervals and hypothesis tests using the hypothesized population mean of 250, showing how the interval includes or excludes the null at a given alpha.

Identify Type I and Type II errors in hypothesis testing, and how the significance level controls the probability of a Type I error, highlighting trade-offs with the null hypothesis.

Explore one tail and two tail hypothesis tests, including how the null mean is tested and rejection regions. Learn to compute p-values in excel using dt dist and rt.

See how to perform a one-tail hypothesis test in Excel, testing whether mean employability scores exceed 300 for placed graduates, using alpha 0.15 and interpreting the p-value 0.045.

Apply the z distribution to tests for proportions by using the difference between sample and hypothesized proportions over the standard error, then report the p-value compared to alpha, with n p-hat > 5.

Apply hypothesis tests for proportions by estimating the share of graduates earning at least 100,000, checking the central limit theorem, and specifying null, alternative, significance, test statistic, and p-value.

conduct a one-sided hypothesis test for a proportion in excel, verifying central limit theorem applicability, using a 0.64 sample proportion to test if >50% at 5% significance.

Compute the difference between each student’s MBA and undergraduate grades, then test the mean difference for dependent samples with a null of zero, using either two-tailed or one-tailed options.

Apply hypothesis tests with dependent samples to evaluate a proposed 50-point employability improvement. Calculate the differences, their mean and standard deviation, and determine hypotheses, significance, test statistic, and p value.

Assess a left-tailed hypothesis test for a 50-point improvement in MBA employability using dependent samples, with a p-value of 0.427, and fail to reject the null.

Explore hypothesis tests for independent samples, calculating the test statistic with a two-sample standard error from both variances and sizes, using t distribution with degrees of freedom, aided by Excel.

Apply a two-sample hypothesis test to compare salaries by work experience, null and alternative hypotheses, compute means, variances, difference in means, degrees of freedom, and p value at 1% significance.

Use an independent samples hypothesis test in Excel at 1% significance to compare salaries of graduates with and without work experience, and conclude no evidence of higher mean salaries.

Evaluate population assumptions with sample statistics by testing the null against the alternative. Set the significance level, balance type I and II errors, and use the p-value to guide decisions.