Lecturer’s desk Physics- atmospheric Sciences (PAS) - Room 201 s c r e e n Row A Row B Row C Row D...

Lecturer’s desk

Physics- atmospheric Sciences (PAS) - Room 201

s c r e e ns c r e e n

Row A

Row B

Row C

Row D

Row E

Row F

Row G

Row H

13 12 11 10 9 8 7 Row A

14 13 12 11 10 9 8 7 Row B

15 14 13 12 11 10 9 8 7 Row C

15 14 13 12 11 10 9 8 7 Row D16

15 14 13 12 11 10 9 8 7 Row E17 16

15 14 13 12 11 10 9 8 7 Row F17 16

15 14 13 12 11 10 9 8 7 Row G17 16

15 14 13 12 11 10 9 8 7 Row H16

18

table

Row A

Row B

Row C

Row D

Row E

Row F

Row G

Row H

15 1417 161819

16 1518 171920

17 1619 182021

18 1720 192122

19 1821 202223

20 1922 212324

18 1720 192122

19 1821 202223

2 14 356

2 14 356

2 14 356

2 14 356

2 14 356

2 14 356

2 14 356

2 14 356

Row J

Row K

Row L

Row M

Row N

Row P2 14 35

2 14 35

2 14 35

2 14 35

2 14 35

15Row J

Row K

Row L

Row M

Row N

Row P

27 2629 2830

25 2427 2628

24 2326 2527

23 2225 2426

25 2427 2628

27 2629 2830

614 13 12 11 10 9 8 716 1518 1719202122

614 13 12 11 10 9 8 716 1518 171920212223

614 13 12 11 10 9 8 716 1518 171920212223

614 13 12 11 10 9 8 71624 18 171920212223 1525

614 13 12 11 10 9 8 71624 18 171920212223 1525

Row Q2 14 3527 2629 2830 614 13 12 11 10 9 8 724 2223 21 - 152537 3639 3840 34 31323335

69 8 713 table1418192021

Hand in

(Optional)

Revised

Memo

MGMT 276: Statistical Inference in Management

Fall 2015

We’ll be starting

this today

Just for Fun AssignmentsGo to D2L - Click on “Content”

Click on “Interactive Online Just-for-fun Assignments”Complete Assignments 1 – 7

Please note: These are not worth any class points and are different from the required homeworks

Schedule of readings

Before next exam: September 24th Please read chapters 1 - 4 & Appendix D & E in Lind

Please read Chapters 1, 5, 6 and 13 in Plous• Chapter 1: Selective Perception• Chapter 5: Plasticity• Chapter 6: Effects of Question Wording and Framing• Chapter 13: Anchoring and Adjustment

By the end of lecture today9/15/15

Questionnaire design and evaluationSurveys and questionnaire design

Correlational methodologyPositive, Negative and Zero correlation

Strength and directionWriting Summaries of results

Homework Assignment

Assignment 4Describing Data Visually using MS Excel

Due: Thursday, September 17th

Designed our study / observation / questionnaire

Collected our data

Organize and present our results

Scatterplot displays relationships between two continuous variables

Correlation: Measure of how two variables co-occur and also can be used for prediction

Range between -1 and +1

The closer to zero the weaker the relationshipand the worse the prediction

Positive or negative

Correlation

Range between -1 and +1

-1.00 perfect relationship = perfect predictor

+1.00 perfect relationship = perfect predictor

0 no relationship = very poor predictor

+0.80 strong relationship = good predictor

-0.80 strong relationship = good predictor

+0.20 weak relationship = poor predictor

-0.20 weak relationship = poor predictor

Height of Mothers by Height of Daughters

PositiveCorrelation

Height ofDaughters

Heig

ht

of

Moth

ers

Positive correlation: as values on one variable go up, so do valuesfor the other variable

Negative correlation: as values on one variable go up, the values

for the other variable go down

Brushing teethby number cavities

NegativeCorrelation

NumberCavities

Bru

shin

gTe

eth

Positive correlation: as values on one variable go up, so do valuesfor the other variable

Negative correlation: as values on one variable go up, the values

for the other variable go down

Perfect correlation = +1.00 or -1.00

One variable perfectly predicts the other

Negativecorrelation

Positivecorrelation

Height in inches and height in feet

Speed (mph) and time to finish

race

Correlation

Perfect correlation = +1.00 or -1.00

The more closely the dots approximate a straight line,(the less spread out they are) the stronger the relationship is.

One variable perfectly predicts the other

No variability in the scatterplot

The dots approximate a straight line

Correlation

Is it possible that they are causally related?

Correlation does not imply causation

Yes, but the correlational analysis does not answer that question

What if it’s a perfect correlation – isn’t that causal?

No, it feels more compelling, but is neutral about causality

Number of Birthday Cakes

Nu

mb

er

of

Bir

thd

ays

Number of bathrooms in a city and number of crimes committed

Positive correlationPositive correlation

Positive correlation: as values on one variable go up,

so do values for other variable

Negative correlation: as values on one variable go up, the values for other variable go down

Linear vs curvilinear relationship

Linear relationship is a relationshipthat can be described best with a straight line

Curvilinear relationship is a relationship that can be described best with a curved line

Correlation - How do numerical values change?

Let’s estimate the correlation coefficient for each of the following

r = +1.0 r = -1.0 r = +.80

r = -.50 r = 0.0

http://neyman.stat.uiuc.edu/~stat100/cuwu/Games.html

http://argyll.epsb.ca/jreed/math9/strand4/scatterPlot.htm

http://neyman.stat.uiuc.edu/~stat100/cuwu/Games.html

r = +0.97

This shows a strong positive relationship (r = 0.97)

between the price of the house and its eventual sales

priceDescription includes:

Both variablesStrength

(weak,moderate,strong)Direction (positive, negative)

Estimated value (actual number)

r = +0.97 r = -0.48

This shows a moderate negative relationship (r = -

0.48) between the amount of pectin in orange juice and its

sweetnessDescription includes:

Both variablesStrength

(weak,moderate,strong)Direction (positive, negative)

Estimated value (actual number)

r = -0.91

This shows a strong negative relationship (r = -0.91) between the distance that a golf ball is

hit and the accuracy of the drive

Description includes:Both variables

Strength (weak,moderate,strong)

Direction (positive, negative)Estimated value (actual

number)

r = -0.91 r = 0.61

This shows a moderate positive relationship (r = 0.61) between the

price of the length of stay in a hospital and the number of services

provided




number)

r = +0.97 r = -0.48

r = -0.91 r = 0.61

Height of Daughters (inches)

Heig

ht

of

Moth

ers

(i

n)

48 52 56 60 64 68 72 76 48 5

2 5

660 6

4 6

8 7

2

This shows the strong positive (r = +0.8) relationship between the

heights of daughters (in inches) with heights of their mothers (in

inches).

Both axes and values are labeled

Both axes have real numbers

listed

Variable name is

listed clearly

Variable name is listed clearly




number)


Heig

ht

of

Moth

ers

(i

n)

48 52 56 60 64 68 72 76 48 5

2 5

660 6

4 6

8 7

2



inches).



listed

Variable name is

listed clearly





number)


Heig

ht

of

Moth

ers

(i

n)

48 52 56 60 64 68 72 76 48 5

2 5

660 6

4 6

8 7

2



inches).



listed

Variable name is

listed clearly





number)


Heig

ht

of

Moth

ers

(i

n)

48 52 56 60 64 68 72 76 48 5

2 5

660 6

4 6

8 7

2



inches).



listed

Variable name is

listed clearly





number)


Heig

ht

of

Moth

ers

(i

n)

48 52 56 60 64 68 72 76 48 5

2 5

660 6

4 6

8 7

2



inches).



listed

Variable name is

listed clearly





number)

1. Describe one positive correlationDraw a scatterplot (label axes)

2. Describe one negative correlationDraw a scatterplot (label axes)

3. Describe one zero correlationDraw a scatterplot (label axes)

Break into groups of 2 or 3Each person hand in own worksheet. Be sure to list

your name and names of all others in your groupUse examples that are different from those is lecture

4. Describe one perfect correlation (positive or negative)Draw a scatterplot (label axes)

5. Describe curvilinear relationshipDraw a scatterplot (label axes)

You have 12 minutes(approximately 2 minutes per example)


Heig

ht

of

Moth

ers

(i

n)

48 52 56 60 64 68 72 76 48 5

2 5

660 6

4 6

8 7

2



inches).



listed

1. Describe one positive correlationDraw a scatterplot (label axes)

2. Describe one negative correlationDraw a scatterplot (label axes)

3. Describe one zero correlationDraw a scatterplot (label axes)

4. Describe one perfect correlation (positive or negative)Draw a scatterplot (label axes)

5. Describe curvilinear relationshipDraw a scatterplot (label axes)

Variable name is

listed clearly





number)

Hand in

Correlatio

n

worksheet

Overview Frequency distributions

The normal curve

Mean, Median,Mode, Trimmed Mean

Standard deviation,Variance, Range

Mean Absolute Deviation

Skewed right, skewed leftunimodal, bimodal, symmetric

Challenge yourself as we work through characteristics of distributions to try to categorize each concept as a measure

of 1) central tendency

2) dispersion or 3) shape

Another example: How many kids in your family?

3

4

82

2

1

4

1

14

2

Number of kids in family1 43 21 84 2 2 14

Measures of Central Tendency(Measures of location)

The mean, median and mode

Mean: The balance point of a distribution. Found by adding up all observations and then dividing by the number of observations

Mean for a sample:

Mean for a population:

ΣX / N = mean = µ (mu)

Note: Σ = add upx or X = scoresn or N = number of scores

Σx / n = mean = x

Measures of “location”Where on the number line the scores tend to

cluster

Measures of Central Tendency(Measures of location)

The mean, median and mode

Mean: The balance point of a distribution. Found by adding up all observations and then dividing by the number of observations

Mean for a sample:

Note: Σ = add upx or X = scoresn or N = number of scores

Σx / n = mean = x


41/ 10 = mean = 4.1

How many kids are in your family?What is the most common family size?


Median: The middle value when observations are ordered from least to most (or most to least)



1, 3, 1, 4, 2, 4, 2, 8, 2, 14

1, 1, 2, 2, 2, 3, 4, 4, 8, 14



148,4,4,2,2,1,




1, 3, 1, 4, 2, 4, 2, 8, 2, 14

2.5

2, 3,1, 2, 4,2, 4, 8,1, 142, 3,1,

Median always has a percentile rank of 50% regardless of shape

of distribution

2 + 3 µ=2.5If there appears to be two

medians, take the mean of the twoMedian

also called the

2nd Quartile

4,


148,4,4,2,2,1,



2,1, 2, 4,2, 4, 8,1, 142,1,

148,4,4,2,2,1, 2, 3,1, 2, 3,


1st QuartileMiddle number of lower half of

scores

Low

er h

alf

Upper

half

3rd QuartileMiddle number of upper half of

scores

3,3,3,

2.52nd Quartile

Middle number of all scores

(Median)

Mode: The value of the most frequent observation


Score f .1 22 33 14 25 06 07 08 19 010 011 012 013 014 1

Please note:The mode is “2” because it is the most frequently occurring score.

It occurs “3” times. “3” is not the mode, it is

just the frequency for the value that is the

mode

Bimodal distribution: If there are two mostfrequent observations

What about central tendency for qualitative data?

Mode is good for nominal or ordinal data

Median can be used with ordinal data

Mean can be used with interval or ratio data

Overview Frequency distributions

The normal curve

Mean, Median,Mode, Trimmed Mean

Challenge yourself as we work through characteristics of distributions to try to categorize each concept as a measure

of 1) central tendency

2) dispersion or 3) shape

Skewed right, skewed leftunimodal, bimodal, symmetric

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