Research reporting powerpoint presentation

Variability and Range of Mean Deviation

1. Understanding Variability: Range and Mean Deviation

2. What is Variability?

3. What is Variability? - Variability is a fundamental concept in statistics that plays a vital role in understanding and interpreting data. It also refers to the extent to which data points in a data set differ from each other.

4. What is Variability? - Variability is a fundamental concept in statistics that plays a vital role in understanding and interpreting data. It also refers to the extent to which data points in a data set differ from each other. It captures the differences or deviations between individual data points, providing insights into the range of values present. Therefore, understanding variability is essential for making informed decisions, drawing accurate conclusions, and uncovering meaningful relationships within the data.

5. Measure of Variability

6. Measure of Variability A measure of variability, also known as a measure of dispersion, is a statistical metric that quantifies the spread or dispersion of a set of data points. It is an important consideration when the properties of the data set are being investigated because it tells how much scores in a data set vary from one another.

7. Measure of Variability A measure of variability, also known as a measure of dispersion, is a statistical metric that quantifies the spread or dispersion of a set of data points. It is an important consideration when the properties of the data set are being investigated because it tells how much scores in a data set vary from one another. Measure of variability are crucial in data analysis as they help in understanding the diversity, range, and consistency of the data.

8. Common measures of variability:

9. Common measures of variability: Range - The range is the full distance between the lowest and highest scores in distribution. It is the easiest and the quickest to compute. It can be classified into two: the exclusive range and the inclusive range.

10. Common measures of variability: Range - The range is the full distance between the lowest and highest scores in distribution. It is the easiest and the quickest to compute. It can be classified into two: the exclusive range and the inclusive range. The exclusive range is the difference between the highest score (HS) and the lowest score (LS) in the distribution, and its formula is ( HS - LS).

11. Common measures of variability: Range - The range is the full distance between the lowest and highest scores in distribution. It is the easiest and the quickest to compute. It can be classified into two: the exclusive range and the inclusive range. The exclusive range is the difference between the highest score (HS) and the lowest score (LS) in the distribution, and its formula is ( HS - LS). The inclusive range is the difference between the highest score and lowest score plus one (HS - LS + 1).

12. Example of solving an inclusive range and exclusive range :

13. Example of solving an inclusive range and exclusive range : -For example, you will consider a data set of score which have a no. of 3,5,7,9,11.

14. Example of solving an inclusive range and exclusive range : -For example, you will consider a data set of score which have a no. of 3,5,7,9,11. To calculate the formula for exclusive range it is HS-LW. To calculate this, it will be shown or solved as: exclusive range = 11 (HS) – 3(LS) = 8, and that will be your no. of score or the amount for exclusive range.

15. Example of solving an inclusive range and exclusive range : -For example, you will consider a data set of score which have a no. of 3,5,7,9,11. To calculate the formula for exclusive range it is HS-LW. To calculate this, it will be shown or solved as: exclusive range = 11 (HS) – 3(LS) = 8, and that will be your no. of score or the amount for exclusive range. While in the inclusive range, the formula will be: Inclusive range = 11(HS) – 3(LW) + 1 = 9, therefore by using the formula you will get the answer for inclusive range which is 9.

16. Mean deviation

17. Mean deviation - Mean deviation is a measure of dispersion or variability that gives a rough estimate of the distances of the individual scores from the mean of the scores. It is the average of the absolute deviations of the scores from the mean. In mathematical form it is

18. To calculate the mean deviation, you typically follow these steps:

19. To calculate the mean deviation, you typically follow these steps: Find the mean of the data set. Calculate the absolute difference between each data point and the mean. Find the average of these absolute difference.

20. To calculate the mean deviation, you typically follow these steps: Find the mean of the data set. Calculate the absolute difference between each data point and the mean. Find the average of these absolute difference. Example 1: Calculate the mean deviation of the following set of scores: 2, 4, 4, 7, 8, 9, 10, 12.

21. To calculate the mean deviation, you typically follow these steps: Find the mean of the data set. Calculate the absolute difference between each data point and the mean. Find the average of these absolute difference. Example 1: Calculate the mean deviation of the following set of scores: 2, 4, 4, 7, 8, 9, 10, 12. Solution: The mean of the scores is 56/8=7.

22. To calculate the mean deviation, you typically follow these steps: Find the mean of the data set. Calculate the absolute difference between each data point and the mean. Find the average of these absolute difference. Example 1: Calculate the mean deviation of the following set of scores: 2, 4, 4, 7, 8, 9, 10, 12. Solution: The mean of the scores is 56/8=7. The mean deviation can be calculated as follows:

23. Here is the step by step process for you to understand more:

24. Here is the step by step process for you to understand more: Find the mean of the data set: -Mean = (2+4+4+7+8+9+10+12) / 8 Mean = 56/8 = 7

25. Here is the step by step process for you to understand more: Find the mean of the data set: -Mean = (2+4+4+7+8+9+10+12) / 8 Mean = 56/8 = 7 2. Calculate the absolute difference between each data point and the mean:(2-7= 5), (4-7= 3), ( 4-7= 3), (7-7= 0), (8-7= 1), (9-7= 2), (10-7=3), (12-7= 5).

26. Here is the step by step process for you to understand more: Find the mean of the data set: -Mean = (2+4+4+7+8+9+10+12) / 8 Mean = 56/8 = 7 2. Calculate the absolute difference between each data point and the mean:(2-7= 5), (4-7= 3), ( 4-7= 3), (7-7= 0), (8-7= 1), (9-7= 2), (10-7=3), (12-7= 5). 3. Find the sum of the absolute difference: -Sum of absolute differences = 5+3+3+0+1+2+3+5 Sum of absolute differences = 22

27. Here is the step by step process for you to understand more: Find the mean of the data set: -Mean = (2+4+4+7+8+9+10+12) / 8 Mean = 56/8 = 7 2. Calculate the absolute difference between each data point and the mean:(2-7= 5), (4-7= 3), ( 4-7= 3), (7-7= 0), (8-7= 1), (9-7= 2), (10-7=3), (12-7= 5). 3. Find the sum of the absolute difference: -Sum of absolute differences = 5+3+3+0+1+2+3+5 Sum of absolute differences = 22 4. Calculate the mean deviation: -Mean Deviation = Sum of absolute differences / Number of data points Mean deviation = 22 / 8 Mean Deviation= 2.75

28. Here is the step by step process for you to understand more: Find the mean of the data set: -Mean = (2+4+4+7+8+9+10+12) / 8 Mean = 56/8 = 7 2. Calculate the absolute difference between each data point and the mean:(2-7= 5), (4-7= 3), ( 4-7= 3), (7-7= 0), (8-7= 1), (9-7= 2), (10-7=3), (12-7= 5). 3. Find the sum of the absolute difference: -Sum of absolute differences = 5+3+3+0+1+2+3+5 Sum of absolute differences = 22 4. Calculate the mean deviation: -Mean Deviation = Sum of absolute differences / Number of data points Mean deviation = 22 / 8 Mean Deviation= 2.75 Therefore, the average distance of the scores from the mean is 2.75.

29. Variability is a fundamental concept in statistics that plays a vital role in understanding the data. The range is the full distance between the shortest and longest scores in distribution. (HS-LS) is the formula for inclusive range. 4. Measure of variability are crucial in data analysis as they help in understanding the diversity, range, and consistency of the data. (HS-LS + 1) is the formula for exclusive range. In solving for the Mean deviation the sign for the total number of scores is big letter N. Mean deviation is a measure of dispersion or variability that gives a rough estimate of the distances of the individual scores from the mean of the scores. The first step in solving mean deviation is to calculate the mean of the scores The second step in solving mean deviation is to f ind the average of these absolute difference. After solving the mean , you will add the individual scores to the mean TRUE or FALSE

30. Test II. Solvings Solve for the exclusive and inclusive range of scores. ( 2, 4, 6, 8, 10, 12, 14) ( 6,7,7,2,9,1,5,13) ( 4, 8, 9, 2, 5, 12) Test III. Solve for the mean deviatition 1. ( 4, 6, 9, 12, 14, 3, 8, 2 ) 2. ( 2, 4, 4, 8, 10, 11 )