Download presentation

Presentation is loading. Please wait.

Published byAbigail Weeks Modified over 3 years ago

1
Copyright © 2010 Pearson Education, Inc. Slide 4 - 1 Suppose that 30% of the subscribers to a cable television service watch the shopping channel at least once a week. You are to design a simulation to estimate the probability that none of five randomly selected subscribers watches the shopping channel at least once a week. Which of the following assignments of the digits 0 through 9 would be appropriate for modeling an individual subscribers behavior in this simulation? a. Assign 0,1,2 as watching the shopping channel at least once a week and 3,4,5,6,7,8, and 9 as not watching. b. Assign 0,1,2,3 as watching the shopping channel at least once a week and 4,5,6,7,8, and 9 as not watching. c. Assign 0,1,2,3,4 as watching the shopping channel at least once a week and 5,6,7,8, and 9 as not watching. d. Assign 0 as watching the shopping channel at least once a week and 1,2,3,4 and 5 as not watching; ignore digits 6,7,8, and 9 e. Assign 3 as watching the shopping channel at least once a week and 1,2,3,4,5,6,7,8 and 9 as not watching.

2
Copyright © 2010 Pearson Education, Inc. Slide 4 - 2 Suppose that 30% of the subscribers to a cable television service watch the shopping channel at least once a week. You are to design a simulation to estimate the probability that none of five randomly selected subscribers watches the shopping channel at least once a week. Which of the following assignments of the digits 0 through 9 would be appropriate for modeling an individual subscribers behavior in this simulation? a. Assign 0,1,2 as watching the shopping channel at least once a week and 3,4,5,6,7,8, and 9 as not watching. b. Assign 0,1,2,3 as watching the shopping channel at least once a week and 4,5,6,7,8, and 9 as not watching. c. Assign 0,1,2,3,4 as watching the shopping channel at least once a week and 5,6,7,8, and 9 as not watching. d. Assign 0 as watching the shopping channel at least once a week and 1,2,3,4 and 5 as not watching; ignore digits 6,7,8, and 9 e. Assign 3 as watching the shopping channel at least once a week and 1,2,3,4,5,6,7,8 and 9 as not watching.

3
Copyright © 2010 Pearson Education, Inc. Chapter 4 Displaying and Summarizing Quantitative Data

4
Copyright © 2010 Pearson Education, Inc. Slide 4 - 4 What type of data is used in bar charts or pie charts? Histograms – Displays quantitative variables by using bars of equal widths with counts on the vertical axis. They do not show data values. Here is a histogram of earthquake magnitudes:

5
Copyright © 2010 Pearson Education, Inc. Slide 4 - 5 A relative frequency histogram displays the percentage of cases in each bin instead of the count. Spaces are gaps in the data. Here is a relative frequency histogram of earthquake magnitudes:

6
Copyright © 2010 Pearson Education, Inc. Slide 4 - 6 TI Tips – Making a Histogram Enter the following scores in L1: 22, 17,18, 29, 22, 22, 23, 24, 23, 17, 21, 25, 20, 12, 19, 28, 24, 22, 21, 25, 26, 25, 16, 27, 22 2 nd STATPLOT, choose Plot1, Enter In the Plot1 screen choose On, select the histogram icon, then specify Xlist:L1 and Freq: 1 Turn off any other graphs and check y= for any equations. ZOOM, select 9:ZoomStat, Enter Not bad, but the scale is a little messy. Reset the scale to sensible values. WINDOW, Xmin = ?, Xmax=? Xscl=? GRAPH (if we hit ZoomStat it will undo our scale) TRACE

7
Copyright © 2010 Pearson Education, Inc. Slide 4 - 7 TI Tips – Making a Histogram Suppose you are given a set of test scores, with two grades in the 60s, four in the 70s, seven in the 80s, five in the 90s and one 100. Enter the group cutoffs 60, 70, 80, 90, 100 in L2. Enter the corresponding frequencies 2,4, 7, 5, 1 in L3. STATPLOT, Xlist:L2, Freq: L3 How would you set up the window?

8
Copyright © 2010 Pearson Education, Inc. Slide 4 - 8 Stem-and-Leaf Displays Stem-and-leaf displays show the distribution of a quantitative variable, like histograms do, while preserving the individual values. Stem-and-leaf displays contain all the information found in a histogram and, when carefully drawn, satisfy the area principle and show the distribution.

9
Copyright © 2010 Pearson Education, Inc. Slide 4 - 9 Stem-and-Leaf Example Compare the histogram and stem-and-leaf display for the pulse rates of 24 women at a health clinic. Which graphical display do you prefer?

10
Copyright © 2010 Pearson Education, Inc. Slide 4 - 10 Dotplots A dotplot is a simple display. It just places a dot along an axis for each case in the data. The dotplot to the right shows Kentucky Derby winning times, plotting each race as its own dot. Dotplots can be displayed horizontally or vertically.

11
Copyright © 2010 Pearson Education, Inc. Slide 4 - 11 Think Before You Draw Before making a stem-and-leaf display, a histogram, or a dotplot, check the Quantitative Data Condition: The data are values of a quantitative variable whose units are known.

12
Copyright © 2010 Pearson Education, Inc. Slide 4 - 12 Shape, Center, and Spread When describing a distribution, make sure to always tell about three things: shape, center, and spread.

13
Copyright © 2010 Pearson Education, Inc. Slide 4 - 13 What is the Shape of the Distribution? 1.Does the histogram have a single, central hump or several separated humps? 2.Is the histogram symmetric? 3.Do any unusual features stick out?

14
Copyright © 2010 Pearson Education, Inc. Slide 4 - 14 Humps 1.Does the histogram have a single, central hump or several separated bumps? Humps in a histogram are called modes. A histogram with one main peak is dubbed unimodal; histograms with two peaks are bimodal; histograms with three or more peaks are called multimodal.

15
Copyright © 2010 Pearson Education, Inc. Slide 4 - 15 Humps (cont.) A bimodal histogram has two apparent peaks:

16
Copyright © 2010 Pearson Education, Inc. Slide 4 - 16 Humps (cont.) A histogram that doesnt appear to have any mode and in which all the bars are approximately the same height is called uniform:

17
Copyright © 2010 Pearson Education, Inc. Slide 4 - 17 Symmetry 2.Is the histogram symmetric? If you can fold the histogram along a vertical line through the middle and have the edges match pretty closely, the histogram is symmetric.

18
Copyright © 2010 Pearson Education, Inc. Slide 4 - 18 Symmetry (cont.) The (usually) thinner ends of a distribution are called the tails. If one tail stretches out farther than the other, the histogram is said to be skewed to the side of the longer tail. In the figure below, the histogram on the left is said to be skewed left, while the histogram on the right is said to be skewed right.

19
Copyright © 2010 Pearson Education, Inc. Slide 4 - 19 Anything Unusual? 3.Do any unusual features stick out? Sometimes its the unusual features that tell us something interesting or exciting about the data. You should always mention any stragglers, or outliers, that stand off away from the body of the distribution. Are there any gaps in the distribution? If so, we might have data from more than one group.

20
Copyright © 2010 Pearson Education, Inc. Slide 4 - 20 Anything Unusual? (cont.) The following histogram has outliersthere are three cities in the leftmost bar:

21
Copyright © 2010 Pearson Education, Inc. Slide 4 - 21 Where is the Center of the Distribution? If you had to pick a single number to describe all the data what would you pick? Its easy to find the center when a histogram is unimodal and symmetricits right in the middle. On the other hand, its not so easy to find the center of a skewed histogram or a histogram with more than one mode.

22
Copyright © 2010 Pearson Education, Inc. Slide 4 - 22 Center of a Distribution -- Median The median is the value with exactly half the data values below it and half above it. It is the middle data value (once the data values have been ordered) that divides the histogram into two equal areas.

23
Copyright © 2010 Pearson Education, Inc. Slide 4 - 23 Spread Are the values of the distribution tightly clustered around the center or more spread out? The range of the data is the difference between the maximum and minimum values: Range = max – min A disadvantage of the range is that a single extreme value can make it very large and, thus, not representative of the data overall.

24
Copyright © 2010 Pearson Education, Inc. Slide 4 - 24 Spread: The Interquartile Range The interquartile range (IQR) lets us ignore extreme data values and concentrate on the middle of the data. To find the IQR, we first need to know what quartiles are…

25
Copyright © 2010 Pearson Education, Inc. Slide 4 - 25 Spread: The Interquartile Range (cont.) Quartiles divide the data into four equal sections. One quarter of the data lies below the lower quartile, Q1 One quarter of the data lies above the upper quartile, Q3. The quartiles border the middle half of the data. The difference between the quartiles is the interquartile range (IQR), so IQR = upper quartile – lower quartile

26
Copyright © 2010 Pearson Education, Inc. Slide 4 - 26 Example: Find Range, Q 1, Q 2 (median), Q 3 and the IQR of 1, 1, 2, 3, 4, 5, 6 Example: Find Range, Q 1, Q 2 (median), Q 3 and the IQR of 1, 1, 2, 3, 4, 5, 6, 7

27
Copyright © 2010 Pearson Education, Inc. Slide 4 - 27 Spread: The Interquartile Range (cont.) The lower and upper quartiles are the 25 th and 75 th percentiles of the data, so… The IQR contains the middle 50% of the values of the distribution, as shown in figure:

28
Copyright © 2010 Pearson Education, Inc. Slide 4 - 28 5-Number Summary The 5-number summary of a distribution reports its median, quartiles, and extremes (maximum and minimum) The 5-number summary for the recent tsunami earthquake Magnitudes looks like this:

29
Copyright © 2010 Pearson Education, Inc. Slide 4 - 29 Center – Symmetric Distributions When we have symmetric data, there is an alternative other than the median … the mean! We use the Greek letter sigma to mean sum and write: The formula says that to find the mean, we add up all the values of the variable and divide by the number of data values, n.

30
Copyright © 2010 Pearson Education, Inc. Slide 4 - 30 Summarizing Symmetric Distributions -- The Mean (cont.) The mean feels like the center because it is the point where the histogram balances:

31
Copyright © 2010 Pearson Education, Inc. Slide 4 - 31 Example: Draw three graphs (skewed left, right and symmetric) and roughly estimate where the mean and median will be located in each.

32
Copyright © 2010 Pearson Education, Inc. Slide 4 - 32 What About Spread? The Standard Deviation A more powerful measure of spread than the IQR is the standard deviation, which takes into account how far each data value is from the mean. A deviation is the distance that a data value is from the mean.

33
Copyright © 2010 Pearson Education, Inc. Slide 4 - 33 What About Spread? The Standard Deviation (cont.) The variance, notated by s 2, is found by summing the squared deviations and (almost) averaging them: The variance will play a role later in our study, but it is problematic as a measure of spreadit is measured in squared units!

34
Copyright © 2010 Pearson Education, Inc. Slide 4 - 34 What About Spread? The Standard Deviation (cont.) The standard deviation, s, is just the square root of the variance and is measured in the same units as the original data.

35
Copyright © 2010 Pearson Education, Inc. Slide 4 - 35 Thinking About Variation When the data values are tightly clustered around the center of the distribution, the IQR and standard deviation will be small. When the data values are scattered far from the center, the IQR and standard deviation will be large.

36
Copyright © 2010 Pearson Education, Inc. Slide 4 - 36 Tell -- What About Unusual Features? If there are multiple modes, try to understand why. If you identify a reason for the separate modes, it may be good to split the data into two groups. If there are any clear outliers and you are reporting the mean and standard deviation, report them with the outliers present and with the outliers removed. The differences may be quite revealing. Note: The median and IQR are not likely to be affected by the outliers.

37
Copyright © 2010 Pearson Education, Inc. Slide 4 - 37 What Can Go Wrong? Dont make a histogram of a categorical variable bar charts or pie charts should be used for categorical data. Dont look for shape, center, and spread of a bar chart.

38
Copyright © 2010 Pearson Education, Inc. Slide 4 - 38 What Can Go Wrong? (cont.) Choose a bin width appropriate to the data. Changing the bin width changes the appearance of the histogram:

39
Copyright © 2010 Pearson Education, Inc. Slide 4 - 39 TI- Tips Calculating Numerical Summaries Enter the following scores in L1: 22, 17,18, 29, 22, 22, 23, 24, 23, 17, 21, 25, 20, 12, 19, 28, 24, 22, 21, 25, 26, 25, 16, 27, 22 Go to STAT, CALC, select 1-Var Stats, Enter Enter again. (If your data werent in L1, you would have to specify where it was before hitting enter.) Cursor down! Sx is the standard deviation

40
Copyright © 2010 Pearson Education, Inc. Slide 4 - 40 TI Tips – Suppose you are given a set of test scores, with two grades in the 60s, four in the 70s, seven in the 80s, five in the 90s and one 100. Enter the group cutoffs 60, 70, 80, 90, 100 in L2. Enter the corresponding frequencies 2, 4, 7, 5, 1 in L3. 1-Var Stats L2, L3 (1-Var Stats Value, Frequencies)

41
Copyright © 2010 Pearson Education, Inc. Slide 4 - 41 Homework Pg. 72 5 – 45 (15 dont do by hand use calculator, skip 37, 41)

Similar presentations

OK

Copyright © 2010 Pearson Education, Inc. Chapter 4 Displaying and Summarizing Quantitative Data.

Copyright © 2010 Pearson Education, Inc. Chapter 4 Displaying and Summarizing Quantitative Data.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Download ppt on mind controlled robotic arms design Ppt on mammals and egg laying animals image Ppt on object oriented technologies Ppt on economic order quantity pdf Ppt on greenhouse effect and global warming Ppt on sri lanka history Download ppt on minimum wages act 1948 Ppt on computer based information system Ppt on local self government rural Ppt on plasma arc cutting