Math 10800.51/R01: Statistics Finding Normal probabilities The following are solution strategies for the Normal Probability Examples from pages 27 -30 of your course notes. When referring to the standard normal tables from your text, I have denoted them b
MATH 10800.51 and R01: Statistics 2013
Spring,
Assignment #7 Interval Estimation and Hypothesis Testing
1. Section 8.1, #40 from text.
2. Section 8.1, #52 from text.
3. Section 8.1, #56 from text.
4. Section 8.1, #68 from text.
5. Section 9.1, #20 from te
MATH 10800.51/R01: Statistics Spring, 2013 Introduction Definition of Statistics The science of collecting, classifying, presenting, analyzing, and interpreting data Descriptive collection, presentation, summarization, and organization of data Graphical M
Sampling Exploratory data analysis (EDA) seeking patterns, interesting details, and conclusions within the data. EDA makes extensive use of graphical descriptive measures. Results of EDA may not generalize to a larger population
Statistical inference meth
Sampling Distributions
Parameter - a numerical descriptive measure of a population
(Sample) Statistic - a numerical descriptive measure calculated from the observations in
a sample
Sampling Distribution - of a statistic calculated from a sample of n measu
Standard Normal Cumulative Probability Table
Cumulative probabilities for NEGATIVE z-values are shown in the following table: z -3.4 -3.3 -3.2 -3.1 -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0
MATH 10800.51 and R01: Statistics
2013
Spring,
Linear Regression
Simple Linear Regression Line quantifies the relationship between two variables; a
line that describes how a response variable (Y) changes as an explanatory variable (X)
changes
theoretical
MATH 10800.51 and R01: Statistics
Spring, 2013
Concept Mapping
What are concept maps?
Concept mapping is a learning tool that helps learners visualize and
conceptualize abstract ideas, deepen conceptual understandings, connect and
integrate new ideas with
Math 10800
Constructing a Histogram
(Using example 4, page 4 of course notes) Ordered data: 18 21 22 22 23 23 23 24 25 25 25 25 26 27 27 27 28 29 29 31 33 38 43 45 52
1.
Determine the number and width of the classes (each class must have the same width).
Handout 8 Introduction to Confidence Intervals
Why give just a point estimate of the population mean ? If one has to give a single number, a point estimate, to estimate the population mean, the best estimate is the sample mean. But why give just a single
pie chart
Pie Chart
Job Sources of Survey Respondents Frequency
Help-wanted ads
56
Executive search firms
44
Networking
280
Mass mailing
20
Job Sources
Mass mailing; 5%
Help-wanted ads; 14%
Executive search firms; 11%
Networking; 70%
Page 1
time series
Ti
MATH 10800.51 and R01: Statistics
Spring, 2013
Detailed Notes on Inference
Inferential statistics is that arm of statistics where we use the information gathered from a sample of data to draw conclusions about the population from which the sample was deri
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC mean median std dev min max
Nashville San Francisco Hiram 38.3 50.7 25.3 41.2 53.8 26.6 49.3 55.0 35.4 59.2 56.1 47.3 68.2 57.6 58.1 75.9 59.4 66.9 79.2 59.5 71.2 77.9 60.4 69.4 72.0 62.6 63.3 60.6 61.9 52.0
Example 3 (from descriptive numerical measures in course notes) Consider the annual performance over a 10-year period of two mutual funds. The rates of return quoted below are calculated by (P1 - P0)/P0, where P0 and P1 are the fund's share prices at the
Math 10800.51/R01: Statistics
Applications of the Sampling Distribution
The following are solution strategies for the Sampling Distribution Examples from pages 32
-35 of your course notes.
Example 1
x
p(x)
0
0.2
1
0.4
2
0.3
3
0.1
1. The mean and standard
Page 1 of 1
T-Distribution Table
T-Distribution
I
I
I
I
I
I
I
df
00
1
2
3
4
5
6
7
8
9
10
11
12
13
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
14
15
16
17
18
19
20
21
22
23
24
25
26
27
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
1/
II
II
II
II