Week 3 quiz Central TendencyWhat test score was the most common in the class? What is the median income for an urban city in the Midwest? Central tendency answers those questions. The goal of central tendency is to find the scores that best represent a set of data and its distribution. It's also a way to analyze trends of any given set of numbers. The common terms related to central tendency are the mean, median, and mode, which you will explore in your textbook. Privitera, G. J. (2018). Statistics for the behavioral sciences (3rd ed.). Thousand Oaks, CA: Sage Publications. Chapter 3, "Summarizing Data: Central Tendency," pages 76–106. This chapter covers the measures of central tendency. You will learn about the characteristics and how to calculate the mean, median, and mode. Understanding how to appropriately apply the different measures of central tendency to various distributions and scales of measurement is of utmost importance. The measures of central tendency are crucial to understanding all other statistics—the backbone of the rest of what we will cover in this course.Central Tendency and Variability Activity. Practice your knowledge of common terms and definitions related to central tendency and variability.VariabilityIn simple terms, variability relates to how different your data is from each other within a data set. If you have a data set that has a large number range with no pattern, it may be difficult to attribute meaning and to find trends in your information. If the numbers are very different from each other, will you know what they are telling you? For example, if a class of 5 students score 42, 59, 68, 84, and 99 after participating in a critical thinking exercise, it will be hard to assume the impact of the exercise because the scores are so varied.Variability is measured by range and standard deviation. We use the range to measure the difference between the largest and smallest number in the data set. Understanding the mean and standard deviation tells us about variability. The standard deviation gives us a sense of the average distance between the numbers in a data set from the mean, showing variability. For example, a researcher measures stress levels of patients when taking different exams. The researcher would measure trends in the levels to attribute meaning to the data. However, if the levels are at 0, 5, and 10, it can be difficult to find trends or patterns in what exam is causing the stress in the patients. However, if the numbers are closer together, the researcher has more ability to find those trends.Privitera, G. J. (2017). Statistics for the behavioral sciences (3rd ed.). Thousand Oaks, CA: Sage Publications. Chapter 4, "Summarizing Data: Variability," pages 106–137. This chapter introduces you to variability, another key concept needed to understand the rest of the way we analyze data. You will learn how to compute and interpret a range, including interquartile range and semi-interquartile range. The variance and standard deviation for a given population and study will also be explored.

A t test involves comparing the average between two different groups. For example, a new curriculum was implemented at a middle school in the 2018–2019 school year in hopes of improving standardized test scores. To determine whether there was a difference in scores, leaders will conduct a t test using the data from the 2018–2019 school year and the previous school year, 2017–2018, as the two different groups. If scores improved, leaders are likely to implement it for the upcoming 2019–2020 school year to continue upward trends in student performance. Please complete the attached worksheet

Paper must be 1 inch all sides, all double spaced, all headings must be in bold, Must be 4 pages long APA format, New times Roman 12 font