Foundations in data analysis: techniques to unlock insights

Explore real wage data with 534 observations to examine gender wage differentials, controlling for designation, industry, education, and experience, using techniques to compare average wages across groups.

Navigate and select data in Excel quickly by using shift and arrow keys to expand selections and control with arrow keys to reach the end of rows or columns.

Learn how to filter and sort data in Excel using table headers, dropdown menus, and the clear command to analyze female wages by industry and create clean subsets for comparison.

Master Excel's average function to compute mean values by referencing cells and selecting ranges; compare it with sum and count, and format results as currency.

discover how to compute subcategory averages in Excel using the average and average ifs functions, including gender-based wage comparisons and multiple criteria.

Discover how to use the =averageifs function to compute an average with multiple criteria, such as gender and industry, in Excel. Compare female and male wages in manufacturing.

Calculate the mean or average for data, distinguish the mean of a sample from the data set average, and understand dispersion and outliers around the mean.

Master calculating average, median, and mode in Excel using cell references and formulas to analyze numerical data and understand when these metrics apply.

Explore basic visualizations for categorical data using bar graphs and pie charts to describe designation and industry breakdowns, highlighting counts, percentages, and how missing categories shape analysis.

Use countif in Excel to tally designations and genders, then create bar charts and pie charts to visualize raw counts and percentage distributions of categorical data.

Analyze wage per hour by gender and designation to compare means and gender wage gap. Recognize that designations do not explain the gap, and beware averages, sample size, and outliers.

Learn to use Excel's countifs to count across multiple criteria. Count designations by category and gender, then graph results and explore percentage breakdowns.

Box plots compare data distributions and outliers between two samples, such as male and female wages. They reveal medians, 25th and 75th percentiles, and how outliers affect the wage gap.

Learn how to create and edit a box plot in Excel, add data labels and a legend, format for readability, remove outliers, and compare wage subsets.

Use scatter plots to show relationships between two variables by plotting each observation on x and y axes, highlighting trends and outliers and the importance of data quality.

Create and customize Excel scatter plots to visualize relationships between two variables, label axes, adjust axes limits, remove outliers, and add a linear trend line for interpretation.

Explain how the Pearson correlation coefficient measures the strength and direction of a relationship between education and wage, from -1 to 1, using scatter plots.

Use Excel's CORREL to calculate the correlation coefficient for wage with education and with work experience, revealing a positive relation with education (≈0.3) and a weak one with experience (≈0.08).

Learn how histograms reveal the shape of data in foundations in data analysis by visualizing frequency distributions, using wage and education data to explore bin sizes and distribution patterns.

Create and edit histograms in Excel with histogram chart function, adjusting bin width and number of bins, adding data labels to reveal peaks and data structure around averages and means.

Explore why statisticians square deviations to measure spread and distinguish volatility across datasets. Compare absolute values and squared differences to see how squaring emphasizes larger deviations relative to the mean.

Learn how to calculate variance by measuring the average squared differences from the mean, using both population and sample formulas, and understand variance as data spread and risk.

Learn how to compute population and sample variance in Excel using built-in functions and bottom-up formulas, with hourly wage data and descriptive statistics from the Data Analysis Toolpak.

Learn to compute standard deviation as the square root of variance, compare population and sample variance formulas, and use one standard deviation to measure spread around mean and identify outliers.

Calculate standard deviation in Excel using population or sample variance and built-in functions. Then use mean and standard deviation to identify outliers and assess data distribution.

Explore the normal distribution and how mean, median, and mode coincide, then use standard deviation to interpret 68 percent within one standard deviation and 95 percent within two standard deviations.

Explore how the central limit theorem makes the sampling distribution of sample means normal even when data are not, enabling confidence intervals and hypothesis tests.

Construct confidence intervals from sample data using mean, standard deviation, sample size, and z-scores, interpreting a 95% interval as the likely range for the true mean.

Apply confidence intervals to wage data to compare male and female salaries across designations and assess 95 percent certainty about gaps and overlaps.

Calculate confidence intervals in Excel using the sample mean, size, and standard deviation, and apply the confidence.norm function to obtain a 95% interval for wage data.

Learn to add confidence intervals to Excel bar graphs by calculating means, standard deviations, and sample sizes, then apply custom error bars to compare male and female wages.

Explore the one-sample t-test to compare a sample mean to a known value, compute the t-value, and decide between null and alternative hypotheses.

Perform a two-tailed one-sample t-test to compare a sample mean to a hypothesized value, with alpha 0.05 for a 95 percent confidence interval, and decide between null and alternative hypotheses.

Explore one-tailed and two-tailed one-sample t-tests, focusing on right- and left-hand side tests, null vs alternative hypotheses, and critical t values with alpha 0.05 for sample versus population means.

Learn how to use p-values to accept or reject the null hypothesis in a one-sample t-test, comparing p-values with alpha and interpreting t-distribution results in Excel.

Apply a one-sample t-test with a one-tailed p-value to assess whether extra training raises average wages, using alpha 0.05, critical t values, and p-values to accept or reject the null.

Compare two samples of wages using a two-sample t-test to assess whether male and female wages differ, test null and alternative hypotheses, and compute the t-value at 95% confidence.

Apply the two-sample t-test to calculate the critical t-value, determine rejection regions at alpha 0.05, and conclude that male wage data differ from female wage data.

Explore the p-value approach to a two-sample t-test, interpret p-values and alpha, and learn how to reject the null hypothesis when p is less than alpha.

Apply wage analysis techniques to compare male and female wages, using sample size checks, confidence intervals, t-tests, and designations controls to uncover gender wage gaps.