Stat 100 Exam 2 Review
Given below are the specific learning outcomes that will be assessed on Exam 2.
Contained in this review are problems that will be similar to the exam. It is highly
recommended that you use this review as a way to assess your own un
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Worksheet # 3: Ch. 7,8,9,10
Multiple Choice
Identify the choice that best completes the statement or answers the question.
_
1. Which of the following is NOT one of the five health-related components of physical fitness?
a. Body composition
b. Muscular st
Title: Practice and Learning
1. The type of experience necessary to produce changes is called _ and the relatively
permanent changes are called _. An example would be shooting soccer penalty kicks
for an hour each day, and then during game time, never mis
Title: Practice and Learning
1. The type of experience necessary to produce changes is called _ and the relatively
permanent changes are called _. An example would be shooting soccer penalty kicks
for an hour each day, and then during game time, never mis
statistics:
Column
n
Mean
Variance Std. dev.
Std. err. Median Range Min Max Q1 Q3
Sort(Verbal 998 588.78056 5788.3901 76.08147 2.4083169
)
590
500 300
800 540 640
table results for Friends:
Count = 1200
Friends
Frequenc
y
Relative
Frequency
No
difference
602
0.50166667
Opposite sex
434
0.36166667
Same sex
164
0.13666667
statistics:
Colum n
n
Verbal
Mean
Variance Std. dev.
Std. err. Median Range Min Max Q1 Q3
998 588.78056 5788.3901 76.08147 2.4083169
590
500 300
800 540 640
Transcoding, Uploading to Vimeo, and Posting 1-minute Films to i-Learn
in Adobe Media Encoder CC 2015
From Phone
1. Download videos from phone to external hard drive. The procedure varies by phone,
so youll need to do some Googling if you dont know how to
mmary statistics for %_grad_on_time:
Group by: College
Colle
ge
n
Mea
n
Varian
ce
Std.
dev.
Std.
err.
Medi
an
Rang
e
Mi
n
Ma
x
Q1
Q3
A
8
60.9
625
142.92
268
11.955
027
4.2267
404
63.75
30.6
43.
2
73.
8
50.
5
71.
1
B
8
71.2
875
9.3726
786
3.0614
831
1.0823
Weighted Moving Average Technique
for
Stationary Models
Situation:
Model has been determined to be stationary for 20 periods
Required:
Forecast for the next 4 periods (21-24) using a 3-Period
Weighted Moving Average with weights .6, .3, .1 respectively
1.
Regression Technique
for
Trend Models
Situation:
Model has been determined by regression (low p-value for 1) to
have linear trend over 20 periods
Required:
Forecast for the next 4 periods (21-24) using regression.
1.
Perform Regression
Values
Periods
Chec
Holt's Technique
for
Trend Models
Situation:
Model has been determined by regression (low p-value for 1) to
have linear trend over 20 periods
Required:
Forecast for the next 4 periods (21-24) using Holt's technique
using = .2 to forecast the level; = .4 t
Regression Technique With Dummy Variables
for
Trend Models With Seasonal Variation
Situation:
Model has been observed to have linear trend and 4-period
seasonal variation over 20 periods.
Required:
Forecast for the next 4 periods (21-24) using regression.
Classical Decomposition Technique
for
Trend Models With Seasonal Variation
Situation:
Model has been observed to have linear trend and 4-period
seasonal variation over 20 periods.
Required:
Forecast for the next 4 periods (21-24) using regression.
1.
Dete
TDIST and TINV
For some reason the developers of Excel wrote the TDIST and TINV
functions to act differently than NORMSDIST and NORMSINV and other
similar functions in Excel.
TDIST
NORMSDIST(z) gives the probability (area) to the left of z.
TDIST gives th
DETERMINING WHETHER TO INCLUDE DUMMY
SEASONAL VARIABLES IN REGRESSION MODELS
BACKGROUND
Consider the following model with Trend and Seasons S1, S2, S3 and S4.
Note: one less season is included in the model:
Y = 0 + 1t + 2S1 + 3S2 + 4S3 +
CASE 1: The p-va
POISSON DISTRIBUTION
Characteristics
A time interval of size t is observed
No two events occur simultaneously
P(k events in the interval) does not depend on when the interval occurs
The time until the next event is independent of when the last event occur
DRAWING PIE CHARTS IN EXCEL 2007
Step 1: Enter data in column A and list categories in column B.
Step 2:Determine frequencies as follows:
=COUNTI F(A2:A67,B2)
H i ghl i ght the A2:A67 par t
Pr ess F4 functi on key to add
$
Step 3: Highlight the entries i
PAIRED DIFFERENCES
Given:
Test:
The data is paired by some common element (date, etc.)
H0:
H1:
1 - 2 or D = 0
1 - 2 or D > 0
Go to Tools
Select Data Analysis
Select t-Test: Paired Two Sample for Means
Hypothesized Mean Difference: H1: 1 - 2 or D > 0
p-v
NORMAL DISTRIBUTION
Characteristics
Bell shaped curve
X-scale: mean = and standard deviation = (The normal distribution)
Z-scale: mean = 0, standard deviation = 1 (The standard normal distribution)
EXCEL
NORMDIST(k, , ,FALSE) = The density at X = k for a
Linear Regression in Excel
SIMPLE LINEAR REGRESSION EXAMPLE
Butlers Trucking Company is an independent trucking Company in southern California. A major portion
of Butlers business involves deliveries throughout its local area. To develop better work sched
Moving Average Technique
for
Stationary Models
Situation:
Model has been determined to be stationary for 20 periods
Required:
Forecast for the next 4 periods (21-24) using a 4-Period
Moving Average
1. In D6 Enter
=AVERAGE(B2:B5)
2. Drag D6
to D7:D22
3. In
Mean Square Error Performance Measure
for
Comparing the Performance of Time Series Models
Situation:
Forecasted values have been determined for all possible time
periods in which there is a time series value
Required:
Determine the mean square error perfo
Mean Absolute Deviation Performance Measure
for
Comparing the Performance of Time Series Models
Situation:
Forecasted values have been determined for all possible time
periods in which there is a time series value
Required:
Determine the mean absolute dev
Last Period Technique
for
Stationary Models
Situation:
Model has been determined to be stationary for 20 periods
Required:
Forecast for the next 4 periods (21-24)
1. In D3 Enter
=B2
2. Drag D3
to D4:D22
3. In D23 Enter
=D22
4. Drag D23
to D24:D25
TIME SERIES FORECASTING
Determining the Model Type
1.
Plot values of the time series (y) vs. period (x).
If plot DOES APPEAR to have long term trend and seasonal components, use
either a regression model with dummy variables (additive model) or classical