Explore descriptive statistics for mutual fund returns using Excel's data analysis tools, computing mean, median, quartiles, percentiles, variance, covariance, standard deviation, skewness, kurtosis, and range.

Explore descriptive statistics in Excel by computing the median, quartiles, and percentiles from a data set; learn to sort data, rank observations, and verify the median with VLOOKUP and commands.

Explore descriptive statistics by computing the mean (X bar), median, quartiles, and percentiles from scratch, then assess mode and a box and whisker diagram to identify skewness in returns data.

Learn to compute variance and standard deviation from the mean, including squaring deviations and using n-1 for samples, with manual steps and Excel shortcuts.

Explore skewness and kurtosis in descriptive statistics, and learn to use standard deviation and coefficient of variation in Excel to compare risk across distributions.

Compute the correlation coefficient by standardizing covariance with the standard deviations of x and y, and interpret its magnitude and sign to assess linear relationships in sample data.

Compute marginal probabilities from the table margins by dividing margin totals (245, 346, 247) by 838, showing the chances of average, high, and low risk funds.

Explore conditional probability by shrinking the sample to a column or row and calculating exact chances from contingency and relative frequency tables.

Compute probability of A or B with P(A) + P(B) − P(A and B). Use high risk vs mid cap and large cap vs low risk as pivot table examples.

Compute the variance of a binomial distribution from squared deviations around the mean, using Excel’s sumproduct. Verify with the formula n p (1−p) for a 50-trial, 0.05 defect rate.

Compute binomial probabilities step by step, including P(X=3), P(X>3), and P(X≥5), using discrete and cumulative methods before moving to Poisson.

Apply the Poisson distribution to calculate and interpret probabilities for counts, such as X=4, X≥3, and X<7, using cumulative sums and total probability, and connects to the upcoming normal distribution.

Explore continuous random variables and density functions, focusing on the normal density function with bell-shaped curves parameterized by mu and sigma, showing how mean and spread affect probabilities.

Explore the uniform distribution with a bank waiting time example; calculate interval probabilities as rectangle areas for 0–120 s, 10–30 s, and 35 s or more, with Excel notes.

Show how larger sample sizes tighten the sample-mean distribution, reduce the standard error, and boost the chance that x-bar falls near the mean, such as between 27.5 and 28.5.

Compute probabilities for the sample proportion using z values and inverse normal, and determine the 90% symmetric interval and its P-values.

Compute a 95% confidence interval for the average force to break insulators using the t distribution with x bar and its standard error from a 30-sample study, noting normality checks.

Conduct a two-tailed test of the population mean (mu=7500) using n=64; with observed mean 7250, z exceeds 1.96, so reject H0 at 5% significance.

Apply two-tailed hypothesis testing for a population mean using z statistics, p-values, and rejection regions, and interpret how a computed z leads to rejecting the null at 0.05.

Conduct a two-tailed hypothesis test for the population mean with unknown sigma, using the t statistic (df = n−1) from a 27-sample to test mu = 45.

Explore one-tailed hypothesis testing of a population proportion with a chrome market-share example. Calculate p-hat and z, compare to 5% significance, and decide whether to reject H0.

Learn to test the equality of two independent population means using a two-sample t test with pooled variance, covering degrees of freedom and decision rules.

This lecture demonstrates a hands-on two-sample t-test for equality of two unknown population means using pooled variance, X-bar and S squared from two samples.

Explore testing the equality of two population means using a two-tailed t test, interpret the p value, and assess normality and small-sample assumptions.

Explore the equality of two population proportions with z tests, the pooled p-bar, and confidence intervals for differences, and compare to the equality of two means before moving to variances.

Learn to test equality of two population variances using the F test: compare sample variances, compute the F statistic, and interpret the F distribution against a two-tailed 5% critical value.

Compute the p value for the F test to assess equality of two population variances. Note the two-tailed p value, degrees of freedom, and normality assumptions behind the test.