Null hypothesis for single linear regression
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The document discusses the null hypothesis for a single linear regression model. It explains that a null hypothesis states that there is no effect or relationship between the independent and dependent variables. For a regression predicting ACT scores from hours of sleep, the null hypothesis would be: "There will be no significant prediction of ACT scores by hours of sleep." The document provides a template for writing the null hypothesis and works through an example applying the template to the relationship between hours of sleep and ACT scores.
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![There will be no significant prediction of [insert
dependent variable] by [insert predictor variable].](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-8-320.jpg)
![Null-hypothesis for this problem:
You have been asked to investigate how well hours of
sleep predicts ACT scores.
There will be no significant prediction of [insert
dependent variable] by [insert predictor variable]](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-11-320.jpg)
![Null-hypothesis for this problem:
You have been asked to investigate how well hours of
sleep predicts ACT scores.
There will be no significant prediction of [insert
dependent variable] by [insert predictor variable]](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-12-320.jpg)
![Null-hypothesis for this problem:
You have been asked to investigate how well hours of
sleep predicts ACT scores.
There will be no significant prediction of [insert
dependent variable] by [insert predictor variable]](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-13-320.jpg)
![Null-hypothesis for this problem:
You have been asked to investigate how well hours of
sleep predicts ACT scores.
There will be no significant prediction of [insert
dependent variable] by [insert predictor variable]](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-14-320.jpg)
![Null-hypothesis for this problem:
You have been asked to investigate how well hours of
sleep predicts ACT scores.
There will be no significant prediction of ACT scores by
[insert predictor variable]](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-15-320.jpg)
![Null-hypothesis for this problem:
You have been asked to investigate how well hours of
sleep predicts ACT scores.
There will be no significant prediction of ACT scores by
[insert predictor variable]](https://image.slidesharecdn.com/null-hypothesisforsinglelinearregression-141002163517-phpapp01-170529110840/85/Null-hypothesis-for-single-linear-regression-16-320.jpg)
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The document discusses null hypotheses for multiple linear regression. It provides two templates for writing null hypotheses. Template 1 states there will be no significant prediction of the dependent variable (e.g. ACT scores) by the independent variables (e.g. hours of sleep, study time, gender, mother's education). Template 2 states that in the presence of other variables, there will be no significant prediction of the dependent variable by a specific independent variable. The document provides an example applying both templates to investigate the prediction of ACT scores by hours of sleep, study time, gender, and mother's education.Null hypothesis for single linear regression
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The previous lecture in Powerpoint explained the general procedure for conducting a hypothesis
test. Now lets go through an example of a hypothesis test to see how it actually works.
Say youve developed a new drug that you think might influence peoples cognitive ability,
although you arent really sure what it will do. (Yes, this is a really bad study!) You give the
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1.Generate H0 and HA
2.Select statistical procedure
3.Select ￥
4.Calculate observed statistic for your data
5.Determine critical statistic
6.Compare (4) and (5)
7.If (4) exceeds (5), reject H0
8.Otherwise, fail to reject H0
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So what are H0 and HA? We dont know whether the drug will make peoples IQs get higher or
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In this case, refers to the mean of the population that our treatment group represents. This is a
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THEORY, they should respond to it in the same way that our 49 participants respond. So we are
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could also have taken this drug.
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At this point, Im just going to tell you that the statistical procedure that you will use is
something called the Z-test. This is a test that is appropriate for testing whether one sample is
different from some specified value when the value of the population standard deviation () is
known. Over the next few days, we will be introduced to some other statistical procedures that
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3.Select ￥
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Say youve developed a new drug that you think might influence peoples cognitive ability,
although you arent really sure what it will do. (Yes, this is a really bad study!) You give the
drug to a sample of N = 49 people and then test their IQ. Say their IQ turns out to be X = 106.
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general population (suggesting that the pill had some effect), or whether they basically look the
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2.Select statistical procedure
3.Select ￥
4.Calculate observed statistic for your data
5.Determine critical statistic
6.Compare (4) and (5)
7.If (4) exceeds (5), reject H0
8.Otherwise, fail to reject H0
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So what are H0 and HA? We dont know whether the drug will make peoples IQs get higher or
lower, so we need to use a 2-tailed or non-directional hypothesis test. Since the mean in the
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theoretical population of people who have taken our IQ pill. Obviously, this population doesnt
exist in reality, because the only people who have ever taken our pill are the 49 people who were
in our study. But IN THEORY, a whole lot of other people could also take this drug, and IN
THEORY, they should respond to it in the same way that our 49 participants respond. So we are
making an inference from our 49 participants to that very large group of people who in theory
could also have taken this drug.
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At this point, Im just going to tell you that the statistical procedure that you will use is
something called the Z-test. This is a test that is appropriate for testing whether one sample is
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known. Over the next few days, we will be introduced to some other statistical procedures that
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3.Select ￥
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5.Determine critical statistic
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7.If (4) exceeds (5), reject H0
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Lets test this question by using the formal steps of hypothesis testing:
1.Generate H0 and HA
2.Select statistical procedure
3.Select ￥
4.Calculate observed statistic for your data
5.Determine critical statistic
6.Compare (4) and (5)
7.If (4) exceeds (5), reject H0
8.Otherwise, fail to reject H0
1. Generate H0 and HA
So what are H0 and HA? We dont know whether the drug will make peoples IQs get higher or
lower, so we need to use a 2-tailed or non-directional hypothesis test. Since the mean in the
general population is 100, we will test whether the mean in our test group is equal to 100.
H0: = 100 HA: 100
In this case, refers to the mean of the population that our treatment group represents. This is a
theoretical population of people who have taken our IQ pill. Obviously, this population doesnt
exist in reality, because the only people who have ever taken our pill are the 49 people who were
in our study. But IN THEORY, a whole lot of other people could also take this drug, and IN
THEORY, they should respond to it in the same way that our 49 participants respond. So we are
making an inference from our 49 participants to that very large group of people who in theory
could also have taken this drug.
2.Select statistical procedure
At this point, Im just going to tell you that the statistical procedure that you will use is
something called the Z-test. This is a test that is appropriate for testing whether one sample is
different from some specified value when the value of the population standard deviation () is
known. Over the next few days, we will be introduced to some other statistical procedures that
are appropriate for different kinds of situations than this, and you will need to choose which one
is the best to use.
3.Select ￥
In many cases, I will just tell you what level to use. But you should understand why a
researcher might choose one level compared to a ...Null hypothesis for a single-sample t-test
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油
The document discusses the null hypothesis for a single-sample t-test. The null hypothesis states that there is no statistically significant difference between the sample and the population from which it was drawn. Researchers conducting a single-sample t-test hope to fail to reject the null hypothesis, meaning the sample is representative of the larger population and results from experiments on the sample could generalize to the population. An example is given of a null hypothesis for a single-sample t-test comparing ACT scores of 30 local teenagers to the overall population of ACT scores.Chapter 7
Chapter 7MaryWall14
油
This document provides an example of conducting a one-sample hypothesis test to determine if the claimed mean battery life of 500 days by a manufacturer is accurate based on a sample of battery data. The null and alternative hypotheses are defined as H0: 亮 = 500 days and HA: 亮 < 500 days. The sample mean of 490 days is calculated and found to be 2.19 standard deviations below the claimed mean. The p-value is the probability of obtaining a sample mean this low or lower if the null hypothesis is true, which is calculated to be 0.0142, a small probability. This provides evidence to reject the manufacturer's claim and conclude the population mean is actually less than 500 days.Null hypothesis for Wilcoxon Test
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The document discusses the null hypothesis for a Wilcoxon test. It explains that a Wilcoxon test compares a dependent variable (e.g. test scores) for the same sample on two different occasions, similar to a paired t-test. The null hypothesis for a Wilcoxon test states that there is no significant difference in the dependent variable between the first and second time it was collected for the sample. Two examples of null hypotheses are provided.More from Amit Sharma (16)
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This document provides an overview of key concepts in hypothesis testing including:
- The null and alternative hypotheses, where the null hypothesis is what we aim to reject or fail to reject.
- The level of significance and critical region, which define the threshold for rejecting the null hypothesis.
- Type I and type II errors, where we aim to minimize both by choosing an appropriate significance level and critical region.
- Common test statistics like z, t, and chi-squared that are used to evaluate hypotheses based on samples.
- The process of hypothesis testing, which involves defining hypotheses, choosing a test statistic and significance level, and making a decision to reject or fail to reject the null based on the critical region.Type of data
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- Type I error occurs when a true null hypothesis is rejected. Type II error is failing to reject a false null hypothesis. Choosing a significance level balances these two errors, with a higher level increasing Type I errors and a lower level increasing Type II errors.
- In medical testing, it is better to make a Type II error and accept a null hypothesis of no drug difference when there actually is a difference, to avoid releasing an ineffective drug. So a lower significance level that increases Type II errors would be chosen.How to Analyse Data
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Clinical case studies and SPSSAmit Sharma
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This document provides guidance on writing and reporting clinical case studies. It discusses the key components of a clinical case study such as structure, data collection, variables, and analytical tools. Clinical case studies should analyze a real patient situation to identify problems, suggest solutions, and recommend the best solution. The document also differentiates between a clinical case study and clinical case report, noting that reports are shorter summaries of an individual patient case. It emphasizes writing for the target journal and audience when composing a case study.Reportinga split plot anova
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The document discusses reporting the results of a split-plot ANOVA in APA style. It provides an example results section that reports the main effects of gender and time as significant but the interaction effect as not significant. It then breaks down each part of the example, explaining what each value represents, such as the F-ratio, degrees of freedom, mean square error, and p-values.T - test independent samples
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The document provides instructions for conducting an independent samples t-test in SPSS. It explains how to specify the grouping and test variables, define the groups being compared, and set options. It also demonstrates running a t-test to compare mile times between athletes and non-athletes using sample data, and interpreting the output, which includes Levene's test for equal variances and the t-test results.T test independent samples
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The document provides instructions for conducting an independent samples t-test in SPSS. It explains how to specify the grouping and test variables, define the groups being compared, and set options. It also demonstrates running a t-test to compare mile times between athletes and non-athletes, checking assumptions, and interpreting the output, including Levene's test for equal variances and the t-test results.Spss paired samples t test Reporting
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The document describes how to conduct and interpret a paired samples t-test in SPSS. It explains that a paired samples t-test is used to compare the means of two related variables measured on the same subjects. It provides an example using reaction time data collected from participants before and after drinking a beer. It outlines the steps to check assumptions, run the t-test in SPSS, and interpret the output, finding that participants had significantly slower reaction times after consuming alcohol.Reporting pearson correlation in apa
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The document discusses how to report the results of a Pearson correlation analysis in APA style. It provides an example of a problem investigating the relationship between the amount of broccoli extract consumed and scores of well-being. It then shows the template for reporting the Pearson correlation, stating the correlation coefficient r and the p-value.Reporting a one way ANOVA
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A One-way ANOVA was conducted to compare the effect of type of athlete on the number of pizza slices eaten. The ANOVA results showed that the effect of type of athlete on number of pizza slices eaten was significant, F(2,66) = 99.82, p = .000.Reporting a paired sample t -test
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The document provides guidance on reporting paired sample t-test results in APA format. It includes an example of how to write the results in a sentence, explaining that there was a significant/not significant difference between the scores for condition 1 (providing the mean and standard deviation) and condition 2 (providing the mean and standard deviation). It also demonstrates how to fill in the t-statistic, degrees of freedom, and p-value using output from SPSS.Reporting a multiple linear regression in apa
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A multiple linear regression was calculated to predict weight based on height and sex. The regression equation was significant and height and sex were significant predictors of weight, explaining 99.3% of the variance. Participants' predicted weight is equal to 47.138 - 39.133 (sex) + 2.101 (height), where height is measured in inches and sex is coded as 0 for female and 1 for male.Reporting a single sample t- test revised
Reporting a single sample t- test revisedAmit Sharma
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The document provides instructions for reporting the results of a single sample t-test in APA format. It includes an example result comparing the mean IQ scores of persons who eat broccoli regularly (M=120, SD=12.2) to the general population. The t-test found a statistically significant difference between the samples, t(22)=7.86, p=0.000.Reporting an independent sample t- test
Reporting an independent sample t- testAmit Sharma
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An independent samples t-test was conducted to compare truck driver drowsiness scores for country music listening and no country music listening conditions. There was a significant difference in scores for country music listening (M=4.2, SD=1.3) and no country music listening (M=2.2, SD=0.84); t(8)=2.89, p=0.02.Reporting a multiple linear regression in APA
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A multiple linear regression was calculated to predict weight based on height and sex. The regression equation was significant and height and sex were significant predictors of weight, explaining 99.3% of the variance. Participants' predicted weight is equal to 47.138 - 39.133 (sex) + 2.101 (height), where height is measured in inches and sex is coded as 0 for female and 1 for male.Recently uploaded (20)
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Null hypothesis for single linear regression
- 1. Null-hypothesis for a Single-Linear Regression Conceptual Explanation Amit Sharma Associate Professor Dept. of Pharmacy Practice ISF COLLEGE OF PHARMACY Ghal Kalan, Ferozpur GT Road, MOGA, 142001, Punjab Mobile: 09646755140, 09418783145 Phone: No. 01636-650150, 650151 Website: - www.isfcp.org
- 2. With hypothesis testing we are setting up a null- hypothesis
- 3. With hypothesis testing we are setting up a null- hypothesis the probability that there is no effect or relationship
- 4. With hypothesis testing we are setting up a null- hypothesis the probability that there is no effect or relationship and then we collect evidence that leads us to either accept or reject that null hypothesis.
- 5. As you may recall, when running a Single-Linear Regression you are attempting to determine the predictive power of one independent variable (hours of sleep) on a dependent variable (test scores).
- 6. Here is a template for a Single Linear Regression Null- Hypothesis:
- 7. Template
- 8. There will be no significant prediction of [insert dependent variable] by [insert predictor variable].
- 9. Here is an example:
- 10. You have been asked to investigate how well hours of sleep predicts ACT scores.
- 11. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of [insert dependent variable] by [insert predictor variable]
- 12. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of [insert dependent variable] by [insert predictor variable]
- 13. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of [insert dependent variable] by [insert predictor variable]
- 14. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of [insert dependent variable] by [insert predictor variable]
- 15. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of ACT scores by [insert predictor variable]
- 16. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of ACT scores by [insert predictor variable]
- 17. Null-hypothesis for this problem: You have been asked to investigate how well hours of sleep predicts ACT scores. There will be no significant prediction of ACT scores by hours of sleep.
- 18. There will be no significant prediction of ACT scores by hours of sleep.