Без темы <<  Starting Guide to Postgraduate Degrees by Research STEEL CUTTING CHARGES  >> Statistics with Economics and Business Applications Review Describing Data with Numerical Measures Measures of Center Some Notations Arithmetic Mean or Average Example Median Example Mode Example Extreme Values Extreme Values Measures of Variability The Range The Variance The Variance The Standard Deviation Two Ways to Calculate the Sample Variance Two Ways to Calculate the Sample Variance Some Notes Measures of Relative Standing Examples Quartiles and the IQR Calculating Sample Quartiles Example Example Using Measures of Center and Spread: The Box Plot Constructing a Box Plot Constructing a Box Plot Constructing a Box Plot Example Example Interpreting Box Plots Key Concepts Key Concepts Key Concepts Key Concepts

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## Statistics with Economics and Business Applications

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1 ### Statistics with Economics and Business Applications

Chapter 2 Describing Sets of Data Descriptive Statistics – Numerical Measures

2 ### Review

I. What’s in last lecture? Descriptive Statistics – tables and graphs. Chapter 2. II. What's in this lecture? Descriptive Statistics – Numerical Measures. Read Chapter 2.

3 ### Describing Data with Numerical Measures

Graphical methods may not always be sufficient for describing data. Numerical measures can be created for both populations and samples. A parameter is a numerical descriptive measure calculated for a population. A statistic is a numerical descriptive measure calculated for a sample.

4 ### Measures of Center

A measure along the horizontal axis of the data distribution that locates the center of the distribution.

5 ### Some Notations

We can go a long way with a little notation. Suppose we are making a series of n observations. Then we write as the values we observe. Read as “x-one, x-two, etc” Example: Suppose we ask five people how many hours of they spend on the internet in a week and get the following numbers: 2, 9, 11, 5, 6. Then

6 ### Arithmetic Mean or Average

The mean of a set of measurements is the sum of the measurements divided by the total number of measurements.

where n = number of measurements

7 ### Example

Time spend on internet: 2, 9, 11, 5, 6

If we were able to enumerate the whole population, the population mean would be called m (the Greek letter “mu”).

8 ### Median

The median of a set of measurements is the middle measurement when the measurements are ranked from smallest to largest. The position of the median is

once the measurements have been ordered.

9 ### Example

The set: 2, 4, 9, 8, 6, 5, 3 n = 7 Sort: 2, 3, 4, 5, 6, 8, 9 Position: .5(n + 1) = .5(7 + 1) = 4th

The set: 2, 4, 9, 8, 6, 5 n = 6 Sort: 2, 4, 5, 6, 8, 9 Position: .5(n + 1) = .5(6 + 1) = 3.5th

10 ### Mode

The mode is the measurement which occurs most frequently. The set: 2, 4, 9, 8, 8, 5, 3 The mode is 8, which occurs twice The set: 2, 2, 9, 8, 8, 5, 3 There are two modes—8 and 2 (bimodal) The set: 2, 4, 9, 8, 5, 3 There is no mode (each value is unique).

11 ### Example

The number of quarts of milk purchased by 25 households: 0 0 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 4 4 4 5

Mean? Median? Mode? (Highest peak)

12 ### Extreme Values

The mean is more easily affected by extremely large or small values than the median.

The median is often used as a measure of center when the distribution is skewed.

13 ### Extreme Values

Symmetric: Mean = Median

Skewed right: Mean > Median

Skewed left: Mean < Median

14 ### Measures of Variability

A measure along the horizontal axis of the data distribution that describes the spread of the distribution from the center.

15 ### The Range

The range, R, of a set of n measurements is the difference between the largest and smallest measurements. Example: A botanist records the number of petals on 5 flowers: 5, 12, 6, 8, 14 The range is

R = 14 – 5 = 9.

Quick and easy, but only uses 2 of the 5 measurements.

16 ### The Variance

The variance is measure of variability that uses all the measurements. It measures the average deviation of the measurements about their mean. Flower petals: 5, 12, 6, 8, 14

17 ### The Variance

The variance of a population of N measurements is the average of the squared deviations of the measurements about their mean m.

The variance of a sample of n measurements is the sum of the squared deviations of the measurements about their mean, divided by (n – 1).

18 ### The Standard Deviation

In calculating the variance, we squared all of the deviations, and in doing so changed the scale of the measurements. To return this measure of variability to the original units of measure, we calculate the standard deviation, the positive square root of the variance.

19 ### Two Ways to Calculate the Sample Variance

Use the Definition Formula:

5

-4

16

12

3

9

6

-3

9

8

-1

1

14

5

25

Sum

45

0

60

20 ### Two Ways to Calculate the Sample Variance

Use the Calculational Formula:

5

25

12

144

6

36

8

64

14

196

Sum

45

465

21 ### Some Notes

The value of s is ALWAYS positive. The larger the value of s2 or s, the larger the variability of the data set. Why divide by n –1? The sample standard deviation s is often used to estimate the population standard deviation s. Dividing by n –1 gives us a better estimate of s.

22 ### Measures of Relative Standing

How many measurements lie below the measurement of interest? This is measured by the pth percentile.

(100-p) %

p %

23 ### Examples

90% of all men (16 and older) earn more than \$319 per week.

? Median

? Lower Quartile (Q1)

? Upper Quartile (Q3)

\$319 is the 10th percentile.

BUREAU OF LABOR STATISTICS 2002

24 ### Quartiles and the IQR

The lower quartile (Q1) is the value of x which is larger than 25% and less than 75% of the ordered measurements. The upper quartile (Q3) is the value of x which is larger than 75% and less than 25% of the ordered measurements. The range of the “middle 50%” of the measurements is the interquartile range, IQR = Q3 – Q1

25 ### Calculating Sample Quartiles

The lower and upper quartiles (Q1 and Q3), can be calculated as follows: The position of Q1 is

The position of Q3 is

once the measurements have been ordered. If the positions are not integers, find the quartiles by interpolation.

26 ### Example

Q1is 3/4 of the way between the 4th and 5th ordered measurements, or Q1 = 65 + .75(65 - 65) = 65.

The prices (\$) of 18 brands of walking shoes: 60 65 65 65 68 68 70 70 70 70 70 70 74 75 75 90 95

Position of Q1 = .25(18 + 1) = 4.75 Position of Q3 = .75(18 + 1) = 14.25

27 ### Example

Q3 is 1/4 of the way between the 14th and 15th ordered measurements, or Q3 = 75 + .25(75 - 74) = 75.25

and IQR = Q3 – Q1 = 75.25 - 65 = 10.25

The prices (\$) of 18 brands of walking shoes: 60 65 65 65 68 68 70 70 70 70 70 70 74 75 75 90 95

Position of Q1 = .25(18 + 1) = 4.75 Position of Q3 = .75(18 + 1) = 14.25

28 ### Using Measures of Center and Spread: The Box Plot

Divides the data into 4 sets containing an equal number of measurements. A quick summary of the data distribution. Use to form a box plot to describe the shape of the distribution and to detect outliers.

The Five-Number Summary: Min Q1 Median Q3 Max

29 ### Constructing a Box Plot

The definition of the box plot here is similar, but not exact the same as the one in the book. It is simpler. Calculate Q1, the median, Q3 and IQR. Draw a horizontal line to represent the scale of measurement. Draw a box using Q1, the median, Q3.

30 ### Constructing a Box Plot

*

Isolate outliers by calculating Lower fence: Q1-1.5 IQR Upper fence: Q3+1.5 IQR Measurements beyond the upper or lower fence is are outliers and are marked (*).

31 ### Constructing a Box Plot

Draw “whiskers” connecting the largest and smallest measurements that are NOT outliers to the box.

32 ### Example

Amount of sodium in 8 brands of cheese: 260 290 300 320 330 340 340 520

33 ### Example

IQR = 340-292.5 = 47.5 Lower fence = 292.5-1.5(47.5) = 221.25 Upper fence = 340 + 1.5(47.5) = 411.25

Outlier: x = 520

34 ### Interpreting Box Plots

Median line in center of box and whiskers of equal length—symmetric distribution Median line left of center and long right whisker—skewed right Median line right of center and long left whisker—skewed left

35 ### Key Concepts

I. Measures of Center 1. Arithmetic mean (mean) or average a. Population mean: m b. Sample mean of size n: 2. Median: position of the median = .5(n +1) 3. Mode 4. The median may be preferred to the mean if the data are highly skewed. II. Measures of Variability 1. Range: R = largest - smallest

36 ### Key Concepts

2. Variance a. Population of N measurements: b. Sample of n measurements: 3. Standard deviation

37 ### Key Concepts

IV. Measures of Relative Standing 1. pth percentile; p% of the measurements are smaller, and (100 - p)% are larger. 2. Lower quartile, Q 1; position of Q 1 = .25(n +1) 3. Upper quartile, Q 3 ; position of Q 3 = .75(n +1) 4. Interquartile range: IQR = Q 3 - Q 1 V. Box Plots 1. Box plots are used for detecting outliers and shapes of distributions. 2. Q 1 and Q 3 form the ends of the box. The median line is in the interior of the box.

38 ### Key Concepts

3. Upper and lower fences are used to find outliers. a. Lower fence: Q 1 - 1.5(IQR) b. Outer fences: Q 3 + 1.5(IQR) 4. Whiskers are connected to the smallest and largest measurements that are not outliers. 5. Skewed distributions usually have a long whisker in the direction of the skewness, and the median line is drawn away from the direction of the skewness.

«Statistics with Economics and Business Applications»