# Final Exam: Statistics and Probability

Math    |    Beginner
• 1 video | 32s
• Includes Assessment
Final Exam: Statistics and Probability will test your knowledge and application of the topics presented throughout the Statistics and Probability track of the Skillsoft Aspire Essential Math for Data Science Journey.

## WHAT YOU WILL LEARN

• describe what statistics, populations, and samples are recognize how metrics such as mean, median and mode describe data summarize the workings a number of probability sampling techniques load data from a CSV file into a pandas DataFrame and perform some initial analysis calculate the mean and median of a distribution using your own function and compare it with the built-in pandas function use Seaborn and Matplotlib to visualize a distribution and where the mean, median, and mode fit in calculate the mean and median of a distribution using your own function and compare it with the built-in pandas function create a balanced sample using random undersampling and oversampling define terms such as event, outcome, and experiment import python libraries needed to work with probabilities simulate the flipping of a coin in Python define joint, marginal, and conditional probability simulate the rolling of two die to test joint probability calculate joint probabilities associated with the rolling of a die calculate the joint probability of dependent variables define the formula of the expected value of a random variable compute conditional probabilities define and understand the Bayes theorem define a Bayesian model in Python explore the probability tables of nodes in a Bayesian network predict values with Bayesian models explore probabilities associated with a Bayesian model create naive Bayes models in Python define descriptive and inferential statistics describe different types of probability distributions and where they occur analyze and visualize data using box plots recognize how data is distributed using histograms and violin plots calculate and visualize confidence intervals using Python estimate a population's mean with confidence intervals describe binomial distributions and generate one using SciPy
• recount binomial distributions and generate one using SciPy analyze a uniform distribution by using cumulative distribution and probability density functions apply Poisson distributions to make estimates in real-life situations use Poisson distributions to make estimates in real-life situations describe normal distributions and their characteristics explain the law of large numbers programmatically recall the symmetrical features of normal distributions describe the fundamentals of hypothesis testing set up null and alternative hypotheses for statistical tests interpret p-values using alpha levels compare and contrast type I and type II errors in hypothesis testing explore one-sided and two-sided T-tests create a function to manually perform a T-test perform the Wilcoxon signed-rank test to compare medians test medians using the Wilcoxon signed-rank test perform T-tests on real-world data recall the assumptions of the two-sample T-test use the two-sample T-test to compare means use Levene’s test to check for equal variances recognize when the Welch’s T-test should be used describe type I and type II errors perform the paired T-test on paired samples use the Welch’s T-test to compare means recognize the use of the Mann-Whitney U-test use the Mann-Whitney U-test outline the use of one-way ANOVA analysis use Tukey’s HSD to know which categories differ significantly use the non-parametric Kruskal-Wallis test outline the use of the two-way ANOVA analysis use two-way ANOVA with interaction between the independent variables

## IN THIS COURSE

• 1.
Statistics and Probability

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