Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 184.108.40.206.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 464917 Bozeman Science
I perform an independent samples t-test on data that have been simulated to correspond to an actual study done by Brody et al. (2004), which tested the hypothesis that individuals who do not smoke would have relatively larger frontal lobes than individuals who do smoke. Something I didn't mention in the video is relevant to causality. Despite the fact that the Brody et al. (2004) investigation found that smokers have relatively smaller frontal lobes than non-smokers, one does not have a basis to infer causality in this case. Get the data here: http://how2stats.blogspot.com.au/2014/03/independent-samples-t-test-data1.html
Views: 604338 how2stats
Hypothesis test for the difference between means of two populations. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/two-sample-inference/two-sample-t-test-means/v/two-sample-t-test-for-difference-of-means?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics. Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc
Views: 50284 Khan Academy
This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.
Views: 18660 CSSLOhioStateU
I demonstrate how to perform and interpret a paired samples t-test in SPSS. I also point out that many people fail to test the homogeneity of variance assumption in the paired samples t-test, but that this can be done relatively easily with a Pitman-Morgan test. paired t-test
Views: 288664 how2stats
Use Student's t-test to compare the means of two samples. However, the formulas that you use depends on whether the samples are paired or unpaired. If unpaired you also have to check for equality of variance. This video maps out a path to each of the three possible t-test formulas.
Views: 27217 Eugene O'Loughlin
This video shows how to conduct a one-sample hypothesis t-test for the mean in Microsoft Excel using the built-in Data Analysis (from raw data). How to load Data Analysis in Excel: https://youtu.be/SqpSwxJ9t2k
Views: 88739 Joshua Emmanuel
In this tutorial, I explain how to perform a t-test on Microsoft Excel 2013. If you are using the freeware OpenOffice, then most of this still applies. The test is really easy to calculate, but understanding the meaning and uses can be frustrating. In this tutorial, I try to explain this with different examples and trying to not get too technical for beginners. Remember, you want a minimum of 10 samples (n=10) for each group of data in order to develop a reasonable bell curve for the t-test to evaluate. Anything less than n=10 does not give us a reasonably accurate calculation. However, the more samples, the better!
Views: 174556 ATOMIC Teacher
In this tutorial we will learn how to carry out t-test using Python. We are going to learn how to perform independent samples t-test using statsmodels and SciPy. Finally, we are going to learn how to carry out paired samples t-test using SciPy. » Make sure you subscribe to the channel if you haven't: http://bit.ly/SUB2EM » Link to code: https://pastebin.com/Kr8a4msr » One example on how to calculate confidence intervalls: https://stackoverflow.com/a/15034143/4127049 » Indpendent samples t-test: 1) Statsmodels: http://www.statsmodels.org/dev/generated/statsmodels.stats.weightstats.ttest_ind.html#statsmodels.stats.weightstats.ttest_ind 2) SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html#scipy.stats.ttest_ind » Paired Samples t-test: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html
Views: 2741 Erik Marsja
This video explains everything you need to know about the t-test for beginners and novices. It also provides a practical example and walks through how to analyze sample dataset. Finally, it shows you how to report and write the results of the t-test. Link to the dataset - https://docs.google.com/spreadsheets/d/1CWZWan8gJVjRHanhkcJ8FXSbbhFwx1XQUnsNTHWBudI/edit?usp=sharing Table of Contents: 01:19 - The D.E.A.R. Method 01:23 - Define: t-test 04:03 - Define: Independent Samples t-test 04:52 - The D.E.A.R. Method 04:59 - Example: Independent Samples t-test 05:09 - The D.E.A.R. Method 05:09 - Example: Independent Samples t-test 07:25 - Example: Dataset 08:31 - Example: Dataset 08:32 - The D.E.A.R. Method 08:43 - Example: Dataset 08:43 - The D.E.A.R. Method 08:44 - Apply: Independent Samples t-test 11:02 - Apply: Independent Samples t-test 11:04 - Apply: Independent samples t-test 11:55 - Apply: Independent samples t-test 15:00 - Apply: Independent samples t-test 15:16 - The D.E.A.R. Method 15:29 - Report: Independent Samples t-test 17:11 - Report: What we know and what we don’t know
Views: 16060 Chris Lam
In this video, we compare the t test in SPSS with the ANOVA. You might be surprised what you find! t test ANOVA P-Value Video Transcript: In this video I want to take a look at the relationship between the independent samples t-test and the one-way ANOVA. Now it may seem that these tests are quite different, it may look like that on the surface, after all they're located in different chapters in intro stats texts and so on, but we'll take a look today at how these tests are actually very similar. So let's go and start with this example here on your screen. Here we have a two group problem where we have volume, where we have 1 and 2, where this corresponds to either a no music condition, or no volume condition, and a high-volume condition. So in this hypothetical study we had 20 people, 10 were randomly assigned to study for an exam with no music, and then 10 were randomly assigned to study for an exam under high volume. And then the dependent variable here is the exam scores. So our independent variable is type of volume, where we have none or high, and then our dependent is exam scores. So let's start by running an independent samples t test. So do that we go to Analyze, Compare Means and then select Independent- Samples T Test. Here we'll move our dependent variable, exam scores, into the Test Variable(s) box. We'll move volume into the Grouping Variable box. And, as we can see here, we have 1s and 2s, so under Define Groups, group 1 was assigned a 1 and group 2 was assigned a 2. Click Continue and then OK. And then here's the results for the independent samples t test. Before we take a look at that, let's go back and now run the One-Way ANOVA. So we'll go to Analyze, Compare Means and then One-Way ANOVA. This time we move exam scores into what's called the Dependent List, and volume into the Factor box, factor standing for the independent variable. Go ahead and click OK. And then now here's the ANOVA results. Now the first thing here that I want you to notice is the p-value. Look at the p-value for the Independent Samples T Test under Equal variances assumed, we have a p-value of .014. Now here, in the ANOVA results, if you look at this p-value, notice what it is. It's the exact same, .014. And just to make sure, let's go ahead and double-click on this table here, double-click on the p-value and we can see it's .013996, and then we'll do the same here for the ANOVA table, and this is also .013996. Well, if the p-values are the exact same, that indicates that we're really running the same test here. So the first thing we can see is that we get the exact same p-value running our two groups under the independent t as we do when we run our two groups under the one way ANOVA. But there's something else here. You might notice here you see a t of 2.722 and an F of 7.407. Well, despite the p-values looking the same, we can see here that t and F definitely are not equal. So, in summary, we had an F of 7.407 and a t of 2.722. Well, while those are not equal, if we square the t, we would actually get the value for F, within rounding error, it's off by a couple one-thousandths of a place just because SPSS rounded. But this relationship does hold: F is equal to t- squared, within rounding error. Or in other words, when we have two groups, F is equal to t-squared. So we saw that the p-values were the same in SPSS and now we can see that F is equal to t-squared. So therefore we could summarize the results as follows here, the one-way ANOVA and t test are equivalent With two groups. They will provide the same answer or decision in terms of the hypothesis test (as they produce the exact same p value). So, in other words, if you reject the null with the ANOVA, you will reject the null with the t test. However, this property only applies with two groups. So, for example, if we had three groups and we ran the ANOVA we got a p-value is let's say .003, for example. If we have three groups and we run the ANOVA, that would require that we run three separate independent samples t tests, group 1 vs. 2, group 1 vs. 3, and group 2 vs. 3. Now if our p-value for the ANOVA was .003, that doesn't mean in any way, shape, or form, that the p-value for any or all of those t-tests will be .003. So the relationship, once again, only holds when there's two groups. But with two groups, we can run the independent samples t, or we can run the one-way ANOVA, as they produce the exact same p value, meaning you'll draw the same conclusion about the hypothesis test. And once again the relationship between F and t is that F equals t-squared with two groups. This concludes the video for looking at the relationship between the independent t and the ANOVA with two groups. Thanks for watching. YouTube Channel: Quantitative Specialists https://www.youtube.com/user/statisticsinstructor Subscribe today!
Views: 51874 Quantitative Specialists
Tutorial on how to calculate a t test for unrelated or independent groups using Microsoft Excel. Playlist on t tests of independent and dependent means and groups http://www.youtube.com/playlist?list=PL8B759A5C1C5C12AF Like MyBookSucks http://www.FaceBook.Com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 138806 statisticsfun
Seven different statistical tests and a process by which you can decide which to use. The tests are: Test for a mean, test for a proportion, difference of proportions, difference of two means - independent samples, difference of two means - paired, chi-squared test for independence and regression. This video draws together videos about Helen, her brother, Luke and the choconutties. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 728854 Dr Nic's Maths and Stats
A tutorial demonstrating how to carry out a T-test in microsoft Excel using the built-in Data analysis tool pack This is part of a series of tutorials designed to help research scientists in the use of certain software applications commonly used in scientific laboratory work. You can find the entire set of tutorial videos here: http://ehealth.kcl.ac.uk/sites/physiology/ The screencast videos have been made by the author (Dr James Clark, King's College London) in response to common questions raised by students on BSc and MSc courses and are recorded using Camtasia Studio. The content is targeted at students of all levels of undergraduate and postgraduate education as well as professional research scientists. If you wish to link to this video on another web site please make sure you credit the author and provide a link to the blog site (shown above) ©2013 James Clark, king's College London. All rights reserved.
Views: 150722 Dory Video
Another way of measuring the difference between two samples is to compare two unrelated samples. In this design, you measure two groups one time; in contrast, the previous paired test measured the same sample two times. With independent samples, you test whether the means of the samples are, not just different, but statistically significantly different.
Views: 42454 Research By Design
Using a Harry Potter theme, I run an independent samples t-test to see if there is a gender difference (witch vs wizard) on OWL exam scores. This is just a quick independent samples t-test tutorial in SPSS 22 that hopefully will help you run the test and interpret the results. Independent samples t-tests are used when there are two groups to compare. For example, gender differences in some variable (e.g., math achievement). The type of question we could answer using an independent samples t-test in SPSS is: Is there a gender difference in math achievement on the SAT? For data, all you'd need is two columns of data in SPSS. Please note, in this case, I'm going to say that in our sample we have ONLY two genders - cismale and cisfemale. The first column would be sorted by gender (e.g., 0 = cismale; 1=cisfemale). The second column would be SAT Quant scores. That's all you need to run an independent samples t-test in SPSS. Also, note: In good, quality research it is highly important to check assumptions for the statistical tests being used--in this case, an independent samples t-test. I'll update this information at another time. You can quickly run an IS t-test in SPSS! Just follow the tutorial and you'll get results that are very easy to understand! :)
Views: 12099 Lori Stephens
I use some made up ice cream sales data vs. temperature data to demonstrate the test of the slope coefficient in a simple regression analysis.
Views: 56670 ProfTDub
We check the significance of the difference in means between 2 samples using a T Test (in Excel). Dataset can be downloaded at www.learnanalytics.in/blog/wp-content/uploads/2014/02/car_sales.xlsx
Views: 13916 Learn Analytics
*Α brief overview of hypothesis tests for 2 sample means. *Equal variances t-test example.
Views: 31615 Joshua Emmanuel
Visit http://www.statisticshowto.com/how-to-do-a-t-test-in-excel-2013/ for more information on t-tests and hypothesis testing
Views: 210953 Stephanie Glen
A lecture on Comparing Groups using Means and Variances in quantitative research by Graham R Gibbs taken from a series on quantitative data analysis and statistics given to undergraduate students at the University of Huddersfield. This is part 1 of 2 and examines when and how to use the independent samples t-test and when to use its non-parametric equivalent, the Mann-Whitney U test using SPSS. My video on doing an independent samples t-test in SPSS is here: http://www.youtube.com/watch?v=_KHI3ScO8sc The video on the Mann-Whitney U test is here: http://www.youtube.com/watch?v=7iTvv3m9d_g Credits: Music: Kölderen Polka by Tres Tristes Tangos is licensed under an Attribution-ShareAlike 3.0 International License. http://freemusicarchive.org/music/Tres_Tristes_Tangos/ Image: Ice-ferns by Schnobby, Wikimedia Commons, licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Views: 7581 Graham R Gibbs
This video shows Dr. Evan Matthews explaining how to calculate a t-test in Excel, and how to interpret those results. The sample data in this video represents the difference between standing and supine (laying on back) heart rate in beats per minute. Link to video of how to perform averages in Excel, and how to do a simple bar graph in Excel. https://youtu.be/1GM4Dt9yoCE Link to video showing how to improve the appearance of this graph to mimic the appearance of graphs typical in physiology research papers. https://youtu.be/toIWMJl_SnE Link to video of how to perform a correlation in Excel. https://youtu.be/9PzkGAWdbYo Link to Dr. Evan Matthews website. https://sites.google.com/site/evanmatthewseportfolio/home
Views: 14207 Vivo Phys - Evan Matthews
This video examines how to interpret the confidence interval for the independent samples t test in SPSS. Confidence intervals can be used instead of the p-value to assess whether or not the test is significant. YouTube Channel: https://www.youtube.com/user/statisticsinstructor Channel Description: For step by step help with statistics, with a focus on SPSS (with Excel videos now too). Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Videos series coming soon include: multiple regression in spss, factor analysis in spss, nonparametric tests in spss, multiple comparisons in spss, linear contrasts in spss, and many more. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In the previous video we ran the independent samples t-test for a problem where we were comparing the no music group versus the high volume group and examining whether there was a significant difference between these two groups in terms of their test scores. When we ran that we had the output that showed a significant difference with the p of .014 between our two groups. Now, alternatively, instead of looking at the p-value we could interpret the ninety-five percent confidence interval and that's what I want to do here. Notice this range does not include zero and you may recall from a previous presentation on the one-sample t that we had said that if zero was not in this range, that indicates that the test is statistically significant; that is, there's a significant difference between our two groups and since this range does not include zero, the test is significant and we can see that confirmed by a p-value of less than or equal to .05. Another way to think about it is if zero was in this range, say this was negative 1.69 and this was 13.11, zero would be in this range if this was negative if you saw that then the p-value here would also be greater than .05. So this is another way to interpret the results of the independent samples t-test - we can look at the ninety-five percent confidence interval and once again if zero is not in the range that means the test is statistically significant whereas if zero is included in this range then the test is not statistically significant. This concludes the presentation on the ninety-five percent confidence interval for the independent samples t-test. Thanks for watching.
Views: 4886 Quantitative Specialists
http://alphabench.com/data/minitab-paired-samples.html Minitab for Beginners - Paired Samples t-test Tutorial. Video walkthrough of using Minitab to conduct a paired samples t-test. This tutorial walks through a simple example in Minitab of testing for a mean difference across two related samples. Includes setup and explanation of test results. These tests are variously referred to as matched samples, matched pairs, paired samples, related samples or repeated measures. This type of test is used to see if there is a statistically significant difference in the mean value of a variable under different conditions, i.e. before and after, and is commonly used in testing treatment efficacy.
Views: 645 Matt Macarty
This video compares the Independent-Samples T Test to the Paired-Samples T Test (Dependent-Samples T Test) using SPSS. The assumptions for both tests are reviewed.
Views: 8890 Dr. Todd Grande
This video describes how to perform one-sample and independent-samples t tests using SPSS.
Views: 11126 Dr. Todd Grande
https://alphabench.com/data/t-test-tutorial.html Download the spreadsheet example used here: https://alphabench.com/data/t.test_function.xlsx Spreadsheet example of Student's t-test. Excel has a number of methods to conduct t-tests. The T.TEST function is a powerful built-in function that can be used to test sample mean differences under three conditions. This tutorial covers the T.TEST function, discussing formulas to test two sample scenarios including: * conducting two sample t-tests with paired samples * conducting independent samples t-tests for equal and unequal variance A comprehensive discussion of T.TEST function in Excel.
Views: 5489 Matt Macarty
http://thedoctoraljourney.com/ This tutorial defines an independent samples t test, provides examples for when this analysis might be used by a researcher, walks through the process of conducting this analysis, and discusses how to set up an SPSS file and write an APA results section for this analysis. For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
Views: 17324 The Doctoral Journey
Short video tutorial on how to calculate a t-test as well as calculating means and SD using the statistical computer program Jmp (JMP, Version 10.0.2. SAS Institute Inc., Cary, NC, 1989-2013). Part two is here: http://www.youtube.com/watch?v=nAsfDNj-Bsw&feature=youtu.be
Views: 27467 kappabeast
Bitcoin donations are welcome: 1GGV3gbJeA83FWmz9hDfPri8EuqcUtodXy Mike's SAS Tutorials - Lesson 8 - The Paired and Two-Sample t Tests This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 8 introduces the paired sample t-test to compare two sample means when they are not independent data. Paired data is often seen as before and after measurements on a subject, or can be related because they are different measurements of the same source (a person, a machine, etc). I also introduce the two-sample t test for data that is independent because the data come from different sources (e.g., differences between genders, two separated groups, two factories, etc). It is important to understand that these are parametric statistics which means that they require "normally distributed data". To test the normality assumption, the use of a QQ-plot helps to visualize if the data is normally distributed. Alternatively, you can use PROC UNIVARIATE to formally test the normality assumption on each variable. In this lesson, I use the example provided by the SAS Help Manual for PROC TTEST and provide explanations as we learn to use this test. Helpful Notes: 1. The TTEST procedure requires the data be normally distributed. 2. The PAIRED statement is required when you want to compare two dependent measurements. 3. The CLASS statement is used when you want to compare measurements of a variable from two different groups (e.g. gender differences). Today's Code: /* I. PAIRED t TEST EXAMPLE */ /* In this example, taken from the SUGI Supplemental Library User's Guide, Version 5 Edition, a stimulus is being examined to determine its effect on systolic blood pressure. Twelve men participate in the study. Each man's systolic blood pressure is measured both before and after the stimulus is applied. */ data pressure; input SBPbefore SBPafter @@; datalines; 120 128 124 131 130 131 118 127 140 132 128 125 140 141 135 137 126 118 130 132 126 129 127 135 ; run; * NOTE: THIS IS PAIRED* DATA BECAUSE THE MEASURES DEPEND ON THE SUBJECT In other words, when you have different measurements that are measured on the same subject, the data is dependent on the subject and is therefore related to each other. As such, two-sample tests are inappropriate because the data are not independent of each other!; /* PAIRED T TEST WITHOUT GRAPHICS */ proc ttest data=pressure; paired SBPbefore*SBPafter; run; /* PAIRED T TEST WITH GRAPHICS */ ods graphics on; proc ttest data=pressure; paired SBPbefore*SBPafter; run; ods graphics off; * Test for Normality of Each Variable Separately; proc univariate data=pressure; var SBPbefore SBPafter; histogram /normal; run; /* II. TWO SAMPLE t TEST EXAMPLE */ /* In the following example, the golf scores for males and females in a physical education class are compared. The sample sizes from each population are equal, but this is not required for further analysis. The scores are thought to be approximately normally distributed within gender. */ data scores; input Gender $ Score @@; datalines; f 75 f 76 f 80 f 77 f 80 f 77 f 73 m 82 m 80 m 85 m 85 m 78 m 87 m 82 ; run; * NOTE: THIS IS INDEPENDENT* DATA BECAUSE THE MEASURES ARE FROM DIFFERENT SUBJECTS In other words, when you have different measurements that are not measured on the same subject, the data is independent of every other subject and is therefore not related to each other. As such, two-sample tests are appropriate because the data are independent of each other!; /* TWO-SAMPLE T TEST WITHOUT GRAPHICS */ proc ttest data=scores cochran ci=equal umpu; class Gender; var Score; run; /* TWO-SAMPLE T TEST WITH GRAPHICS */ ods graphics on; proc ttest data=scores cochran ci=equal umpu; class Gender; var Score; run; ods graphics off; * Test for Normality of A Single Variable Across Different Classes; proc univariate data=scores; class gender; var score; histogram /normal; run;
Views: 37599 Mike's SAS Tutorials
Learn how to conduct the one-sample t-test and confidence interval for the mean of a single variable. You will learn to use "t.test", "boxplot", "attributes" and "$" commands. This video is a tutorial for programming in R Statistical Software for beginners. You can access and download the "LungCapData" dataset here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a quick overview of the topics addressed in this video: 0:00:11 when is it appropriate to use one sample t-test and confidence interval 0:00:35 how to conduct the one-sample t-test and the confidence interval in R using the "t.test" command 0:00:41 how to access the Help menu in R for the t-test 0:01:05 how to test a null and one-sided alternative hypothesis for the mean with a one-sided confidence interval in R using "t.test" command and "alt" argument 0:02:40 how to produce a two-sided hypothesis test and confidence interval in R, setting the "alternative" ("alt") argument to "two.sided" 0:03:16 how to create a 99 percent confidence interval in R using the "conf" argument 0:03:46 how to see different attributes of an object in R using the "attributes" command 0:03:59 how to extract specific attributes of an object in R using the dollar sign ($)
Views: 90427 MarinStatsLectures-R Programming & Statistics