
Explore inferential statistics by learning how to make inferences from samples about populations, test hypotheses, and understand null and alternative hypotheses, type one error, type two error, and decision rules.
Explore the chi square test of independence, also known as Pearson's chi squared test, to detect relationships between two categorical variables and the necessary assumptions, with a smartphone brand example.
Import Erin's bodyweight data, run a one-sample t test against 400 grams, and interpret the results using the test statistic, p value, and 95% confidence interval.
Interpret a one-sample t-test comparing herring weight to 400 g. Report a mean of 369.55 g with p=0.02 and a 95% confidence interval.
Import and configure data, run an independent samples test to compare mean spending by gender, and interpret 95 percent confidence interval, noting females spend more and the difference is significant.
Explore Pearson correlation, measuring the strength and direction of association between interval scale variables like age and income, and learn key assumptions: linear relationship, no outliers, and normality.
Learn simple linear regression to predict a dependent variable from an independent variable, clarify correlation not causation, and review assumptions like continuous data, linearity, outliers, independence, and homoscedasticity.
Explore how study hours predict exam scores using simple linear regression, interpreting R, R squared, ANOVA significance, and the regression equation.
Conduct a multiple linear regression to predict exam score from spent revising and anxiety level, assess model significance with ANOVA, and interpret which predictors are significant in the coefficient table.
Welcome to "Statistics for Data Analysts and Scientists" - the ultimate course to help you master the practical and business applications of essential statistical tests and concepts!
Are you struggling to make sense of statistical tests like the Chi-Square test of independence, t-tests, correlation, and Analysis of Variance (ANOVA)? Are you looking to understand how these tests can be applied in real-world situations, and how they can be used to drive critical business decisions?
This comprehensive course is designed to equip you with the knowledge and skills to excel as a data analyst or scientist. You will learn how to conduct and interpret key statistical tests such as the one-sample t-test, independent sample t-test, dependent sample t-test, correlation, simple and multiple linear regression, and one-way ANOVA. You will also gain a deep understanding of key statistical concepts like homoscedasticity of variance, multicollinearity, and homogeneity of variance.
With an exciting and engaging teaching style, this course will take you on a journey of discovery that will transform your understanding of statistics. You will learn through a combination of theory, practical examples, and case studies that will help you understand how statistical tests can be applied in real-world situations.
By the end of this course, you will have the confidence and expertise to apply statistical tests and concepts to drive critical business decisions. You will be able to use statistical tools to gain insights into complex data sets, and you will be equipped with the skills to communicate these insights to key stakeholders.
So, what are you waiting for? Sign up for "Statistics for Data Analysts and Scientists" today, and take the first step towards becoming a master of statistical analysis!