Launch this Learning Path to begin your analytics education journey with the Minitab Education Hub.
To be successful in today’s data-driven world, everyone needs to have a basic understanding of data analysis. Data literacy is the ability to derive meaningful information from data, and by 2023, Gartner estimates that, “data literacy will become an explicit and necessary driver of business value”. This learning path emphasizes sound statistical approaches to exploring data, describing important characteristics, as well as creating and interpreting compelling visualizations. You will explore a broad range of graphical and statistical tools to assess if results are on target, investigate differences between groups, and explore relationships between variables. Specifically, discover the fundamentals of important graphical and statistical concepts, such as histograms, bar charts, boxplots, scatterplots, hypothesis testing, confidence intervals, analysis of variance, correlation, and simple regression. All learning is presented using animated lessons, quizzes, and hands-on exercises to help you easily learn statistics and how to use Minitab Statistical Software.
- Use graphs and descriptive statistics to gain insight into objects, processes, events and people.
- Distinguish among different types of data.
- Choose the most appropriate visual representation for a given type of data and for the question we are trying to answer.
- Choose the most appropriate descriptive statistics to summarize different aspects of a given data set, such as the central tendency and spread.
- Examine populations by looking at a subset of the population – a sample – and using inferential statistics.
- Define data collection methods that allow you to draw conclusions with a known level of risk.
- Define the sampling distribution, an important theoretical construct statistic, and explain its impact on how well a sample statistic estimates a population parameter.
- Identify basic characteristics of the normal distribution and use them to calculate how well a sample mean estimates a population mean.
- Establish hypotheses about business problems and use statistics to test those hypotheses.
- Use a 1-sample t-test to determine whether a population mean is equal to a hypothesized value.
- Use a 2 variances test to determine whether two populations have the same variance for a given parameter.
- Use a 2-sample t-test to determine whether two populations have equal means.
- Use a paired t-test to determine whether two dependent populations have equal means.
- Use a 1 proportion test to determine whether a population proportion is equal to a hypothesized value.
- Use a 2 proportions test to determine whether the two populations have equal proportions.
- Use a chi-square test to determine whether the values of the two categorical variables are related.
- Detect significant differences in the mean responses from two or more groups.
- Use individual value plots to visualize within- and between-group variation and identify group means.
- Identify groups whose mean responses differ from the mean responses of other groups in the set.
- Detect significant differences in a mean response due to either of two factors or to the interaction between those factors.
- Detect significant differences in the mean responses from two or more groups.
- Use individual value plots to visualize within- and between-group variation and identify group means.
- Identify groups whose mean responses differ from the mean responses of other groups in the set.
- Detect significant differences in a mean response due to either of two factors or to the interaction between those factors.
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