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- https://stats.libretexts.org/Courses/Cerritos_College/Introduction_to_Statistics_with_R/03%3A_Summarizing_Data_Visually/3.02%3A_Quantitative_Datahist(variable, col="type in what color you want", number of classes, main="type the title you want", xlab="type the label you want for the horizontal axis") – produces histogram with specified color a...hist(variable, col="type in what color you want", number of classes, main="type the title you want", xlab="type the label you want for the horizontal axis") – produces histogram with specified color and number of classes (though the number of classes is an estimate and R will create the number of classes near this value).
- https://stats.libretexts.org/Courses/Highline_College/Statistics_Using_Technology_(Kozak)/02%3A_Graphical_Descriptions_of_Data/2.02%3A_Quantitative_DataTo create a cumulative frequency distribution, count the number of data points that are below the upper class boundary, starting with the first class and working up to the top class. Also, if you want...To create a cumulative frequency distribution, count the number of data points that are below the upper class boundary, starting with the first class and working up to the top class. Also, if you want to know the amount that 15 students pay less than, then you start at 15 on the vertical axis and then go over to the graph and down to the horizontal axis where the line intersects the graph.
- https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Statistics_with_Technology_2e_(Kozak)/02%3A_Graphical_Descriptions_of_Data/2.02%3A_Quantitative_Datahist(variable, col="type in what color you want", number of classes, main="type the title you want", xlab="type the label you want for the horizontal axis") – produces histogram with specified color a...hist(variable, col="type in what color you want", number of classes, main="type the title you want", xlab="type the label you want for the horizontal axis") – produces histogram with specified color and number of classes (though the number of classes is an estimate and R will create the number of classes near this value).
- https://stats.libretexts.org/Courses/Fort_Hays_State_University/Elements_of_Statistics/02%3A_Descriptive_Statistics/2.01%3A_Descriptive_Statistics_and_DistributionsTo get a firm grasp on a set of data generally requires several descriptive statistics and a method of visualization. Two very different data sets may have the same values for certain descriptive stat...To get a firm grasp on a set of data generally requires several descriptive statistics and a method of visualization. Two very different data sets may have the same values for certain descriptive statistics while differing for others. A good place to start is to see how the data is distributed. Frequency and relative frequency distributions are quite common and informative.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/06%3A_The_Normal_Distribution/6.03%3A_Estimating_the_Binomial_with_the_Normal_DistributionThis page discusses estimating binomial processes using various probability distributions, particularly the normal, Poisson, and hypergeometric distributions. It notes that the normal distribution eff...This page discusses estimating binomial processes using various probability distributions, particularly the normal, Poisson, and hypergeometric distributions. It notes that the normal distribution effectively approximates binomial outcomes when both np and n(1-p) exceed five. An example involving Australian Shepherd puppies illustrates these calculations for binomial probabilities, highlighting the utility of the normal distribution in simplifying computations.
- https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Statistics_Through_an_Equity_Lens_(Anthony)/01%3A_Chapters/1.02%3A_Descriptive_StatisticsThis chapter focuses on the foundational role of descriptive statistics in data-equity projects, emphasizing the need to collect and understand data to address specific issues. Descriptive statistics ...This chapter focuses on the foundational role of descriptive statistics in data-equity projects, emphasizing the need to collect and understand data to address specific issues. Descriptive statistics involve organizing, describing, and summarizing data using various statistical methods such as measures of central tendency, variability, and distribution shapes. The chapter also highlights the significance of data disaggregation to uncover equity gaps.
