# Final Exam: Statistics and Probability

Math    |    Beginner
• 1 video | 32s
• Includes Assessment
Rating 4.7 of 3 users (3)
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

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