Research Statistics: Scratch to Master

Explore data visualization basics using line charts to compare laptop and tablet sales from January to June, and understand simple versus multiple line charts for presenting insights to management.

Master tabulation by understanding table types, captions, dates, headings, units, footnotes, and sources, and prepare data for analysis using tools like SPSS.

Explore ogives (continued) and reading class boundaries within class intervals, plot cumulative frequencies on graph paper, and determine the median from the graphical limit.

Explore quartile deviation for grouped data with continuous class intervals using Q1, Q3, and cumulative frequency. Compute the coefficient of quartile deviation from Q1 and Q3 using the formulas.

Explain mean deviation from the center with deviations from mean, median, or mode. State the coefficient of mean deviation as deviation about M divided by M, in percent.

Learn to compute the composite standard deviation for two groups by using group means and standard deviations, and apply the combined mean formula, as shown in the example.

Learn to compute the coefficient of variance from a missing frequency in a grouped distribution. Use mean 16.4 and total 72 to determine the missing frequency and the CV.

Compute the correlation coefficient for X and Y using the given data and the standard formula, and explore covariance to interpret a very high positive relationship.

Learn how covariance measures the association between two variables and how the correlation coefficient standardizes it, indicating a strong positive relationship (about 0.91) with a practical example.

This lecture introduces the minimum least squares method for fitting a line by minimizing the sum of squared errors, illustrated with data points and y = a + b x.

Explore the limitations of linear regression through Ascombe's quartet, showing how outliers and data visualization affect the regression line, and how residuals and normal distribution tests reveal model validity.

Explore the Poisson distribution, a discrete model for counts with average lambda, using p(x=k)=e^{-lambda} lambda^k / k!, and apply it to deliveries in a 4–5 pm interval.

Explore the Poisson distribution's application to rare events, derive lambda from conditions, and compute mean and standard deviation for Poisson random variables.

Learn how to compute a Z-score from X minus μ over σ, convert to the standard normal, and interpret the percentile using an Infosys test example.

Learn how to formulate one-way and two-way anova hypotheses, including null, alternative, and interaction hypotheses, using a two-factor example of month and gender.