π Statistics is a mathematical science used to analyze data and make informed decisions.
π Descriptive statistics focuses on organizing and summarizing data, while inferential statistics draws conclusions and models relationships.
π Statistics has a wide-ranging impact on various aspects of our lives, from daily routines to running cities.
π Variables can be quantitative or qualitative, and can be discrete or continuous.
π There are four types of statistical measures used to describe data: frequency, central tendency, spread, and position.
π SAS provides a list of procedures for performing descriptive statistics, such as proc print, proc mean, and proc frequency.
π The video discusses the use of SAS data sets for conducting statistical analyses, including the creation of various types of charts and box plots.
π Descriptive statistics are used to analyze the mean, standard deviation, and other values of the imported data set.
π The video also introduces the concept of inferential statistics and hypothesis testing to determine if certain conditions hold true for the entire population.
π Hypothesis testing involves choosing between two competing hypotheses: the null hypothesis and the alternative hypothesis.
π Variables in statistics are classified into four types: nominal, ordinal, interval, and ratio variables.
π Ratio scales have a true zero point and provide additional properties.
π Performing hypothesis testing using SAS with an example.
π Differentiating between parametric and non-parametric tests in hypothesis testing.
π The video introduces various parametric tests in statistics for data science, including t-test, ANOVA, chi-square, and linear regression.
π§ͺ The t-test is used to determine if two sets of data are significantly different from each other, and it can be applied in different scenarios.
π ANOVA is a generalized version of the t-test and is used to check variance between two or more groups.
π Chi-square test is used to compare observed data with expected data according to a specific hypothesis.
π’ Linear regression includes simple linear regression and multiple linear regression, which are used to predict relationships between variables.
π‘ Simple linear regression and multiple linear regression are used to find relationships between variables.
π Wilcoxon rank sum test and Kruskal-Wallis H-test are non-parametric tests used to compare samples.
π Parametric tests provide information about the population and are easier to use, but have limitations.
π Non-parametric tests are simple to understand and make fewer assumptions, but are less efficient.
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