基础统计学(英文版)

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Chapter 3 Probability Statistics 统计学英文教材

Event: { Die is even }={ 2 4 6 }
A subset of the sample space.
Outcome:
Larson/Farber Ch. 3
{4} The result of a single trial
3
Another Experiment
Probability Experiment: An action through which
= 400 / 1000 = 0.4 = 450 / 1000 = 0.45 =250 / 1000 = 0.25 = 95 / 250 = 0.38 Answers: 1) 0.4 2) 0.45 3) 0.25 4) 0.38
Larson/Farber Ch. 3 15
Miami 150 95 5 250
Empirical
P(E) = Frequency Total of event Frequency E
Probability blood pressure will decrease after medication
Intuition
Probability the line will be busy
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Conditional Probability
The probability an event B will occur, given (on the condition) that another event A has occurred.
We write this as P(B|A) and say “probability of B, given A”.
{1, 2, 3, 4, 5, 6}
The conditional probability, P(B|A) = 1/6

《统计学基础(英文版·第7版)》教学课件les7e_ppt_04_02 (1)

Chapter 4
Discrete Probability Distributions
Copyright 2019, 2015, 2012, Pearson Education, Inc.
1
Chapter Outline
• 4.1 Probability Distributions • 4.2 Binomial Distributions • 4.3 More Discrete Probability Distributions
P(x)
nCx pxqnx
n!
pxqnx
(n x)!x!
• n = number of trials
• p = probability of success
• q = 1 – p probability of failure
• x = number of successes in n trials
surgeries)
.
Copyright 2019, 2015, 2012, Pearson Education, Inc.
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Example: Identifying and Understanding Binomial Experiments
Decide whether each experiment is a binomial experiment. If it is, specify the values of n, p, and q, and list the possible values of the random variable x. If it is not, explain why.
n = 3, p = 190, q = 110, and x = 2.

《统计学基础(英文版·第7版)》课件les7e_ppt_ADA_0304

Slide 17
Example: Finding Probabilities (2 of 3)
Find the probability of being dealt 5 diamonds from a standard deck of 52 playing cards.
Solution In a standard deck of playing cards, 13 cards are diamonds. Note that it does not matter what order the cards are selected. The possible number of ways of choosing 5 diamonds out of 13 is 13C5.
Slide 7
Example: Finding presubscript n P subscript r (1 of 2)
Find the number of ways of forming four-digit codes in which no digit is repeated.
Solution:
• You need to select 4 digits from a group of 10
•
n 10, r 4
10
P4
10
10 4
10 6
10 9 8 7 6 6
5040 ways
Copyright 2019, 2015, 2012, Pearson Education, Inc.
Solution: The number of permutations is
9 9 8 7 6 5 4 3 2 1 362,880 ways

统计学经典书籍推荐

统计学经典书籍推荐这是我碰巧在网上看到有人做了一些关于统计学经典书籍推荐和建议的总结，所以特意转载与此,希望对大家有用。

一、统计学基础部分1、《统计学》David Freedman等著,魏宗舒，施锡铨等译中国统计出版社据说是统计思想讲得最好的一本书，读了部分章节,受益很多。

整本书几乎没有公式，但是讲到了统计思想的精髓。

2、《Mind on statistics(英文版）》机械工业出版社只需要高中的数学水平，统计的扫盲书.有一句话影响很深：Mathematics as to statistics is somethinglike hammer，nails, wood as to a house，it’s just the material andtools but not the house itself。

3、《Mathematical Statistics and Data Analysis(英文版.第二版）》机械工业出版社看了就发现和国内的数理统计树有明显的不同。

这本书理念很好,讲了很多新的东西，把很热门的Bootstrap方法和传统统计在一起讲了。

Amazon上有书评。

4、《Business Statistics a decision making approach（影印版)》中国统计出版社在实务中很实用的东西，虽然往往为数理统计的老师所不屑5、《Understanding Statistics in the behavioral science（影印版）》中国统计出版社和上面那本是一个系列的.老外的书都挺有意思的6、《探索性数据分析》中国统计出版社和第一本是一个系列的。

大家好好看看陈希儒老先生做的序,可以说是对中国数理统计的一种反思。

二、回归部分1、《应用线性回归》中国统计出版社还是著名的蓝皮书系列，有一定的深度，道理讲得挺透的。

看看里面对于偏回归系数的说明,绝对是大开眼界啊！非常精彩的书2、《Regression Analysis by example （3rd Ed影印版)》这是偶第一本从头到底读完的原版统计书，太好看了。

基础统计学培训讲座(英文版)(ppt 31页)

• A population might consist of all the people in Nanaimo but also may mean the PE ratios for all chemical stocks, or total assets of the 20 largest banks in North America, total collection of prices, ages, square footage of retail space in Nanaimo, and so on.
1-18
Types of Variables
A continuous variable can assume any value within a specified range.
Examples are: The pressure in a tire, the weight of a pork chop, or the height of students in a class. Typically, continuous variables are the result of measuring something.
1-11
Types of Statistics
Inferential Statistics: The methods used to determine something about a population, based on a sample.
EXAMPLE 3: Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale.

统计学英文

统计学英文Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In this article, we will discuss the key concepts and principles of statistics.Sample and PopulationStatistics is based on the idea of sampling. A sample is a subset of a population that is selected for analysis. The population is the entire group that is the subject of the study. For example, if we want to study the average age of university students in a country, the population is all the university students in the country. We cannot study the entire population, so we select a sample of students from different universities and use statistics to make inferences about the population based on the sample.Descriptive and Inferential StatisticsDescriptive statistics is concerned with summarizing and describing data. It includes measures of central tendency such as mean, median, and mode, and measures of variability such as range and standard deviation. Descriptive statistics helps us understand the characteristics of the data.Inferential statistics, on the other hand, is concerned with making conclusions about a population based on a sample. It involves testing hypotheses and estimating parameters. For example, we may want to test the hypothesis that the average age of university students in the country is 20 years. We would select a sample of students, calculate the sample mean, anduse statistical tests to determine whether the difference between the sample mean and the hypothesized population mean is significant.Variables and Data TypesA variable is a characteristic of a population or a sample that can take on different values. There are two types of variables: quantitative and qualitative. Quantitative variables are numerical, such as age, weight, and height. Qualitative variables are categorical, such as gender, ethnicity, and occupation.Data can be collected in different ways, such as through surveys, experiments, and observations. Data can also be classified into different types: nominal, ordinal, interval, and ratio. Nominal data are categorical, such as gender or race. Ordinal data are ranked, such as academic achievement or social status. Interval data are numerical, such as temperature or time, but lack a true zero point. Ratio data are numerical and have a true zero point, such as weight or height.Measures of Central TendencyMeasures of central tendency are used to summarize the data and provide a single value that represents the typical score. The three most commonly used measures of central tendency are the mean, median, and mode.The mean is the arithmetic average of the scores. It is calculated by adding up all the scores and dividing by the number of scores. The mean is sensitive to outliers, or extreme scores, which can skew the results.The median is the middle score when the scores are arranged in order. It is not affected by outliers and is a better measure of central tendency when the distribution is skewed.The mode is the most common score. It is useful for nominal data and can be used with ordinal data.Measures of VariabilityMeasures of variability are used to describe the spread or dispersion of the data. The most commonly used measures of variability are the range, variance, and standard deviation.The range is the difference between the largest and smallest scores. It is affected by outliers and is not a very reliable measure of variability.The variance is a measure of how much the scores deviate from the mean. It is calculated by subtracting each score from the mean, squaring the differences, and averaging the squares. The variance is not as intuitive as the other measures of variability, but it is useful for statistical analysis.The standard deviation is the square root of the variance. It is a more intuitive and commonly used measure of variability. The standard deviation is useful for determining how much the scores deviate from the mean and for estimating confidence intervals.Hypothesis TestingHypothesis testing is a process of determining whether a statement about a population is likely to be true or false based on a sample of data. The statement is called a null hypothesis, and the alternative to the null hypothesis is called the alternative hypothesis. We collect data and use statistics to test the null hypothesis.We use a significance level, or alpha, to determine whether the results are statistically significant. If the p-value is less than the significance level, we reject the null hypothesis and accept the alternative hypothesis.ConclusionStatistics is a powerful tool for analyzing and interpreting data. Understanding the concepts and principles of statistics is essential for making informed decisions and drawing accurate conclusions from data.。

《统计学基础(英文版·第7版)》教学课件les7e_ppt_08_01

▪ Independent Samples • The sample selected from one population is not related to the sample selected from the second population.
▪ Dependent Samples (paired or matched samples) • Each member of one sample corresponds to a member of the other sample.
.
Copyright 2019, 2015, 2012, Pearson Education, Inc.
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Two Sample Hypothesis Test with Independent Samples
1. Null hypothesis H0 ▪ A statistical hypothesis that usually states there is no difference between the parameters of two populations. ▪ Always contains the symbol , =, or .
.
Copyright 2019, 2015, 2012, Pearson Education, Inc.
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Two Sample z-Test for the
Difference Between Means
A two-sample z-test can be used to test the difference
Solution: These samples are dependent. Because the triglyceride levels of the same patients are taken, the samples are related. The samples can be paired with respect to each patient.

基础统计学英文课件 (6)

Section 1.2
Data Classification
Section 1.2 Objectives
• Distinguish between qualitative data and quantitative data • Classify data with respect to the four levels of measurement
What is Statistics?
Statistics The science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
Data Sets
Population The collection of all outcomes, responses, measurements, or counts that are of interest. Sample A subset of the population.
Section 1.1 Summary
• • • • Defined statistics Distinguished between a population and a sample Distinguished between a parameter and a statistic Distinguished between descriptive statistics and inferential statistics
Statistic
A number that describes a sample characteristic. Average age of people from a sample of three states

统计学基础(第4章总量指标与相对指标)

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（二）货币单位
用货币单位来作为计量事物数量的统计指标称为价值指标。如国内生产总值、进出口贸易额、工业增加值、工资总额、销售收入、利润等。
（三）劳动单位是用劳动消耗时间来表示的计量单位，如工时、工日等。
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四、计算和使用总量指标应注意的问题（一）要注意现象的同类性（二）要有明确的统计含义和统计方法（三）要统一计量单位
第四章总量指标与相对指标
Fundamentals of Statistics
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教学目的与要求：
本章主要介绍统计指标的意义、种类及其计算和应用。通过学习要求掌握： 1.总量指标的概念、作用及种类； 2.相对指标的概念、作用及常见相对指标的特点及计算方法.
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2、度量衡单位，如t(吨),kg(千克）m(米），立方米，平方米等。
3、双重或多重计量单位，如台/kw,台/t等。
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4、复合单位，是将两种计量单位结合在一起以乘积表示某事物数量的计量单位，如发电量用kw·h, 货物周转量用t·km表示。
5、标准实物单位，是按照一定的折算标准来度量被研究对象数量的一种计量单位。例如，各种氮肥以含氮量100%、为标准单位进行折算等。
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三、总量指标的计量单位
总量指标表现一定社会经济现象的具体数值，有一定的计量单位，一般分为实物单位、劳动单位和价值单位。（一）实物单位，是根据事物的属性和特点而规定的计量单位。它一般有五种：
1、自然单位是根据事物的自然表现形态来度量其数量的单位。如人口按人，汽车按辆等。

统计学英文版教材课件

Combining Events
There are some important ways in which events can be combined that we will encounter repeatedly throughout this course. Suppose we have two events, A and B .
For example, A ∪ B = {1, 3, 4, 5}.
S A 1 5 2
STAT7055 - Lecture 2
B 3 4
6
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Introduction
Intersection, Union and Complement
Complement
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February 17, 2016
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Introduction
Deﬁnitions
Probabilities of Outcomes
The probability of an outcome occurring on a single trial is written as P (Oi ). Probabilities associated with the outcomes in a sample space must satisfy two important requirements:
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Introduction
Events
Events
A simple event is an individual outcome from the sample space. An event is a collection of one or more simple events (or outcomes).

统计学基础 Basics of Statistics

Class Structure
Instructors
Jobayer Hossain, Ph.D. - Biostatistician Tim Bunnell, Ph.D. - Psychologist
8 Classes 3 Take-home assignments
Assigned in classes 2, 4, and 6 Due in classes 3, 5, and 7
Possible special topics
Microarray analyses Pattern Recognition Machine Learning Hidden Markov Modeling Time series analysis Others?
Basics of Statistics
Frequency, percentage or proportion of each category
Some Definitions
Variable - any characteristic of an individual or entity. A variable can take different values for different individuals. Variables can be categorical or quantitative. Per S. S. Stevens…
1 t
Assigned in class 8 -- return for final comments.
Class Participation
Default dataset
60 subjects 3 or 4 groups Several measures of different types

《统计学基础(英文版·第7版)》教学课件les7e_ppt_04_03

12
Solution: Finding a Poisson Probability Using a Table
According to the table, the probability is 0.0425. You can check this result using technology. As shown below using Excel, the probability is 0.042484. After rounding to four decimal places, the probability is 0.0425, which is the same value found using the table.
Chapter 4
Discrete Probability Distributions
Copyright 2019, 2015, 2012, Pearson Education, Inc.
1
Chapter Outline
• 4.1 Probability Distributions • 4.2 Binomial Distributions • 4.3 More Discrete Probability Distributions
.
Copyright 2019, 2015, 2012, Pearson Education, Inc.
11
Solution: Finding a Poisson Probability Using a Table
Solution: A portion of Table 3 in Appendix B is shown here.
The mean number of accidents per month at a certain intersection is 3. What is the probability that in any given month four accidents will occur at this intersection?

基础统计学英文课件 (10)

In Symbols
State H0 and Ha.
Identify .
n = total number of + and – signs
Performing The Paired-Sample Sign Test
In Words
4. Determine the critical value.
z (40 0.5) 0.5(100) 1.9 100 2
• Decision: Fail to Reject H0
At the 1% level of significance you cannot reject the dealership’s claim.
The Paired-Sample Sign Test
775 765 801 742 754 753 739 751 745 750 777 769 756 760 782 789
Solution: Using the Sign Test
• H0: median ≤ 750 • Ha: median > 750
• Compare each data entry with the hypothesized median 750
• Right-tailed test: H0: median k and Ha: median > k
• Two-tailed test: H0: median = k and Ha: median k
Sign Test for a Population Median
• To use the sign test, each entry is compared with the hypothesized median k. § If the entry is below the median, a sign is assigned. § If the entry is above the median, a + sign is assigned. § If the entry is equal to the median, 0 is assigned.

基础统计学(英文版)(ppt 31页)

EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,…,etc). But you cannot have 2.3 bedrooms or 10.6 hammers…Thus discrete variables result from counting.
1-3
Why study statistics?
• Numerical info is everywhere
– But how do we know if conclusions reported are accurate?
• Statistical techniques are used to make decisions that affect our lives
1-18
Types of Variables
A continuous variable can assume any value within a specified range.
Examples are: The pressure in a tire, the weight of a pork chop, or the height of students in a class. Typically, continuous variables are the result of measuring something.
1-15
Types of Variables
For a Qualitative or Attribute variable the characteristic being studied is nonnumeric.

《统计学基础(英文版·第7版)》课件les7e_ppt_ADA_1102

A golf club manufacturer claims that golfers can lower their scores by using the manufacturer’s newly designed golf clubs. The table shows the scores of 10 golfers while using the old design and while using
Copyright 2019, 2015, 2012, Pearson Education, Inc.ilcoxon Signed-Rank Test (1 of 4)
In Words
In Symbols
1. Verify that the samples are random and dependent.
Slide 11
Solution: Performing a Wilcoxon Signed-Rank Test (2 of 3)
• From Table 9 in Appendix B, the critical value is 8. To find the test statistic ws , complete a table as shown below.
Elementary Statistics
Seventh Edition
Chapter 11
Nonparametric Tests
Copyright 2019, 2015, 2012, Pearson Education, Inc.
Slide 1
Chapter Outline
• 11.1 The Sign Test • 11.2 The Wilcoxon Tests • 11.3 The Kruskal-Wallis Test • 11.4 Rank Correlation • 11.5 The Runs Test

统计学英文教学课件Chapter 1(商科)What is Statistics

Judgement Convenience
Probability Samples
Simple Random
Systematic
Stratified
Cluster
16
Simple Random Sampling
• Every possible subset of n units has the same chance of being selected
and levels of measurement 3. Describe key data collection methods 4. Identify common sampling methods 5. Distinguish the different areas of statistics 6. Explain why you study statistics
11
Data Collection Issues - Errors
Sampling
Bad Luck
Non-sampling
Interviewer/Instrument Bias Non-response Bias Selection Bias Interviewee Lie Measurement Error Observer Bias
8
Levels of Measurement
Interval
Similar to ordinal data, WITH differences between data values being meaningful, BUT ratio of two data values not meaningful
N/n = 5000/200 = 25. Select a random number from 1 to 25. Suppose you randomly select the 16th student. Then select every 25th student from there: 41, 66, 91, …

基础统计学英文课件 (8)

population mean • Determine the minimum sample size required when
estimating μ
Point Estimate for Population μ
Point Estimate • A single value estimate for a population parameter • Most unbiased point estimate of the population mean
Level of Confidence
• If the level of confidence is 90%, this means that we are 90% confident that the interval contains the population mean μ.
c = 0.90
Margin of error
• The greatest possible distance between the point estimate and the value of the parameter it is estimating for a given level of confidence, c.
Solution: Finding the Margin of Error
• First find the critical values
0.95
0.025
0.025
z
-zc = -1z.c96 z = 0 zczc= 1.96
95% of the area under the standard normal curve falls within 1.96 standard deviations of the mean. (You can approximate the distribution of the sample means with a normal curve by the Central Limit Theorem, because n ≥ 30.)

基础统计学英文课件 (3)

Average that have share price increases on a given day.
Solution: Discrete random variable (The number of stocks whose share price increases can be counted.)
Score, x 1 2 3 4
Frequency, f 24 33 42 30
5
21
Solution: Constructing a Discrete Probability Distribution
• Divide the frequency of each score by the total
Solution: Continuous random variable (The amount of water can be any volume between 0 ounces and 32 ounces)
x
0 1 2 3 … 32
Discrete Probability Distributions
§ x = Number of sales calls a salesperson makes in one day.
x
0 12 345
Random Variables
Continuous Random Variable • Has an uncountable number of possible outcomes,
employees. Individuals were given a score from 1 to 5,
where 1 was extremely passive and 5 extremely

基础统计学英文版

Types of Statistics
Descriptive Statistics: Methods of organizing, summarizing, and presenting data in an informative way.
EXAMPLE 2: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2002. The statistic 9 describes the number of problems out of every 100 machines.
Types of Variables
A continuous variable can assume any value within a specified range.
Examples are: The pressure in a tire, the weight of a pork chop, or the height of students in a class. Typically, continuous variables are the result of measuring something.
Population vs. Sample
Population is the entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest.