Lesson IX: ESTIMATION AND CONFIDENCE INTERVALS
I.
INTRODUCTION:
Now we shall study inferential statistics; the branch of statistics that helps us make
decisions about some characteristics of a population based on sample information.
Estimation and hypothe
LESSON VII: Continuous Random variables and the Normal
Distribution
Introduction
A continuous random variable can assume any value in an interval. Because the number of
values contained in an interval can not be counted; so these can not be listed as was
1. The population distribution is the probability distribution of the population data.
2. statistic is a numerical measure (like mean, median, variance etc.) calculated from
the sample data. The sampling distribution of a statistic (such as sample mean or
STAT 251 DL/ Mid-term Review
Note: answers are the underlined numbers on the right side
1. A stem leaf display was obtained as follows:
1| 1 2 2
2| 3 4 4 5 6 7 8 8 8 9
3| 0 2 3 4
4| 0 1
9| 3
a. How many items are there in the sample?
20
b. Find the value
Lesson VI: RANDOM VARIABLES AND DISCRETE PROBABILITY DISTRIBUTIONS
By the end of this lesson you should be able to
Define random variable and probability distribution
State the characteristics of a probability distribution. Identify unusual values of a ra
STATISTICS CHAPTER 1 TEST
Name:
1. Under descriptive statistics, we study:
a. decision making tricks
c. methods for organizing, displaying, and describing
data
b. how to collect surveys
d. how to describe the probability distribution
2. In statistics, a p
1
LESSON VI: Random Variables
DISCRETE DISTRIBUTIONS
Introduction
In this Lesson we shall study about random variables, their probability distributions, and
characterization using mean and variance etc. Then we shall study in detail the most commonly used
Lesson I: INTRODUCTION
I. THE NATURE OF STATISTICS
1.1 Essential Elements of Statistics
1. WHAT IS STATISTICS?
In plural sense it means data with specified characteristics, and in singular sense it means statistical
methods.
It is the science of conductin
Lesson I: INTRODUCTION
By the end of this lesson, you should be able to
Explain what statistics is and be familiar with its terminology and basic tools.
Define descriptive and inferential statistics and give examples of situations that
represent each
Defi
Name: _
Period: _
CHAPTER 5 REVIEW
YOU MUST SHOW ALL WORK ON A SEPARATE SHEET OF PAPER OR YOU
WILL NOT RECEIVE CREDIT
1. Number of accidents that occur annually on a busy intersection is an example of:
a. continuous random variable
b. discrete random vari
Name: _ Date: _
A)
B)
C)
D)
1.The experiment of tossing a coin 4 times has
2 outcomes
16 outcomes
8 outcomes
6 outcomes
A)
B)
C)
D)
2.Which of the following values cannot be the probability of an event?
0.58
1.31
0.45
1.00
Use the following to answer ques
Lesson III: DATA DESCRIPTION Numerical
Measures
INTRODUCTION
One of the main objectives of statistics is to draw conclusions about the
characteristics of a population based on data collected from a sample. It is difficult to
work with the complete distrib
Lesson III: NUMERICAL MEASURES
By the end of this lesson you should be able to
Determine measures of central tendency for a given set of data or a grouped or
ungrouped frequency distribution, and summarize data using them.
Look at the graph of given distr