Statistics Essentials Training for Business and Data Science

Learn essential Excel functions by summing values in two columns labeled X and Y using SUM and SUMIFS, and dragging the bottom right corner to copy formulas for data analysis.

Learn to compute average, standard deviation, and correlation between two columns in Excel using formulas or the data analysis toolpak for descriptive statistics.

Visualize relationships between X and Y in Excel by creating scatterplots, bar charts, and pie charts, customizing data ranges, trend lines, titles, legends, and colors for clear data presentation.

See how statistics help in collecting, organizing, analyzing, interpreting, and presenting data to reveal meaning in patterns and variations, and gain a helicopter view of statistics in the scientific method.

Design studies to answer business questions by collecting data and describing it with graphs. Distinguish surveys from experiments, avoid bias, and understand correlation versus causation with a control group.

Collecting data requires random selection to avoid bias and garbage in, garbage out, using methods like random phone polling to minimize biased results.

Learn to summarize data using descriptive statistics and charts, including frequency, relative frequency, measures of center and spread, and relationships between two variables, with pie and bar charts.

Analyze data using appropriate statistical methods and interpret results accurately. Understand how confidence levels and margins of error relate sample results to the population.

Explore basic probability to quantify uncertainty and inform decisions, covering experiments, trials, elementary outcomes, events, sample space, and the coin toss example of heads and tails.

Explain how simple probability assigns equal likelihood to outcomes in the sample space. Show how the law of large numbers ties relative frequency to theoretical probability and biased coins.

Explore factorial and binomial coefficient concepts, and see how combinatorics explains possible outcomes through permutations, variations, and combinations.

Learn how order matters in permutations, from three books to larger sets, with and without repetition, and apply basic permutation formulas to count options.

Explore how to count variations for selecting president, secretary, and treasurer from 25 kids, using formulas for variations with and without repetition, yielding fifteen thousand six hundred twenty-five variations.

Explore selecting two athletes from a twenty-member team for a TV speech, where order doesn't matter. Compare combinations without repetition (190) and with repetition (3990).

Explore descriptive statistics and data types—quantitative, qualitative, and ordinal—with examples from surveys and measurements to guide initial interpretation and future analysis.

Explore central tendency measurements that identify the central point of a dataset. Learn the primary measures—mean, mode, and median—with the weighted mean as an additional summary statistic.

Learn how the mean describes the data midpoint and is the average, with an example 10, 5, 8, 3 equals 6.5, and how trimming outliers addresses extreme values.

Identify the median as the dataset’s middle and compare it to the mean. Compute the average of the two middle values, as with 50 and 49, reducing sensitivity to outliers.

Explore the mode as a central tendency technique that identifies the most frequently occurring value, and examine its use with discrete outcomes in website rating systems and customer reviews.

Compute the weighted mean by multiplying each value by its weight, summing the products to reflect varying importance, such as grades or assignments with different weights.

Evaluate how the spread reveals dataset variability and affects mean representativeness. Explore range, variance, and standard deviation to see why smaller variation improves accuracy.

The range measures spread by calculating the difference between the maximum and minimum values in a data set. Extreme min or max values can indicate data entry errors.

Learn how standard deviation measures data dispersion by calculating the average squared distance from the mean, illustrated with a sample dataset and the sigma formula.

Explore empirical technique to visualize data with histograms and the normal distribution, including the standard normal form, sigma ranges, and data driven Six Sigma improvement.

Examine z-scores as the distance from the mean in units of standard deviation using the formula (x - mean)/sigma, and identify anomalous points when deviations reach three.

Understand confidence intervals by choosing a percent confidence level for the mean, and apply the margin of error and interval identification formulas.

Learn when to use the t-score with small samples or unknown population standard deviation, and how degrees of freedom (n minus one) shape the t distribution.

Statistics helps confirm or deny a hypothesis through testable experiments or observations. Form if-then hypotheses with independent and dependent variables, and ensure results are replicable with clear criteria.

Identify the null hypothesis and its alternative, select the appropriate test, and decide to reject or support the null to validate survey or experiment results.

Apply hypothesis testing to interpret p values as evidence against the null hypothesis and compare them to alpha levels. Understand p values as tail probabilities, expressed as decimals or percentages.

Explore alpha levels and significance in hypothesis testing, comparing p-values to the chosen alpha to decide when to reject the null hypothesis and balance type I and II errors.

Apply regression analysis to predict outcomes from data, using a linear model with x as the independent variable, slope m and intercept b, and interpret R-squared value for model quality.

Explore simple linear regression, the most widely used method to model the relationship between two variables and predict with a linear equation, using Excel to create scatterplots and check fit.

Learn to perform simple linear regression in Excel by enabling the data analysis toolpak, preparing two-column data, running regression, and viewing the regression equation, R-squared, and trendlines with scatterplots.

Learn how the correlation coefficient tests relationships between variables and enables predictions. Explore two Excel methods—formula and data analysis tool pack—for calculating correlation, with values from -1 to 1.

Use pie charts to visualize categorical data by percentage, ensuring slices sum to 100 percent and noting other categories as less detailed; verify that the dataset total is reported.

Bar graphs summarize categorical data like a pie chart and compare groups by displaying side-by-side bars; watch y-axis units, scale, whether percentages or counts are shown, and consider the total.

Explore histograms as a bar-graph tool for numerical data to visualize distribution, symmetry, skewness, center, and variability, while evaluating vertical scales and units for accurate interpretation.

Learn how statistical process control, the genesis of Six Sigma, uses control charts to monitor a process’s health by tracking its average and distribution, avoiding misused specifications with SPC.