Final Exam: Statistics and Probability
Math | Beginner
- 1 video | 32s
- Includes Assessment
- Earns a Badge
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 arerecognize how metrics such as mean, median and mode describe datasummarize the workings a number of probability sampling techniquesload data from a csv file into a pandas dataframe and perform some initial analysiscalculate the mean and median of a distribution using your own function and compare it with the built-in pandas functionuse seaborn and matplotlib to visualize a distribution and where the mean, median, and mode fit incalculate the mean and median of a distribution using your own function and compare it with the built-in pandas functioncreate a balanced sample using random undersampling and oversamplingdefine terms such as event, outcome, and experimentimport python libraries needed to work with probabilitiessimulate the flipping of a coin in pythondefine joint, marginal, and conditional probabilitysimulate the rolling of two die to test joint probabilitycalculate joint probabilities associated with the rolling of a diecalculate the joint probability of dependent variablesdefine the formula of the expected value of a random variablecompute conditional probabilitiesdefine and understand the bayes theoremdefine a bayesian model in pythonexplore the probability tables of nodes in a bayesian networkpredict values with bayesian modelsexplore probabilities associated with a bayesian modelcreate naive bayes models in pythondefine descriptive and inferential statisticsdescribe different types of probability distributions and where they occuranalyze and visualize data using box plotsrecognize how data is distributed using histograms and violin plotscalculate and visualize confidence intervals using pythonestimate a population's mean with confidence intervalsdescribe binomial distributions and generate one using scipy
recount binomial distributions and generate one using scipyanalyze a uniform distribution by using cumulative distribution and probability density functionsapply poisson distributions to make estimates in real-life situationsuse poisson distributions to make estimates in real-life situationsdescribe normal distributions and their characteristicsexplain the law of large numbers programmaticallyrecall the symmetrical features of normal distributionsdescribe the fundamentals of hypothesis testingset up null and alternative hypotheses for statistical testsinterpret p-values using alpha levelscompare and contrast type i and type ii errors in hypothesis testingexplore one-sided and two-sided t-testscreate a function to manually perform a t-testperform the wilcoxon signed-rank test to compare medianstest medians using the wilcoxon signed-rank testperform t-tests on real-world datarecall the assumptions of the two-sample t-testuse the two-sample t-test to compare meansuse levene’s test to check for equal variancesrecognize when the welch’s t-test should be useddescribe type i and type ii errorsperform the paired t-test on paired samplesuse the welch’s t-test to compare meansrecognize the use of the mann-whitney u-testuse the mann-whitney u-testoutline the use of one-way anova analysisuse tukey’s hsd to know which categories differ significantlyuse the non-parametric kruskal-wallis testoutline the use of the two-way anova analysisuse two-way anova with interaction between the independent variables
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