one-sample-t-test

There are 0 repository under one-sample-t-test topic.

• MoinDalvs / Learn_hypothesis_testing_for_Data_science

  Hypothesis for Data Science

  one-sample-t-testtwo-sample-t-testindependent-sample-t-testpaired-sample-t-testproportion-testanova-testchi2-contingencychi2goodness-of-fittestofindependence
  Language:Jupyter Notebook 1

• vaitybharati / P15.-Hypothesis-Testing-1S1T---Super-Market-Loyality-Program

  Hypothesis-Testing 1S1T-Super-Market-Loyality-Program. Population Parameters: Mean=120 Sample Parameters: n=80, Mean=130, SD=40, df=80-1=79

  pythonhypothesis-testingone-sample-t-testscipystatsnormt-scorep-valuesignificance-testing
  Language:Jupyter Notebook 1

• vaitybharati / P16.-Hypothesis-Testing-1S2T---Call-Center-Process

  Hypothesis Testing 1S2T - Call Center Process. Sample Parameters: n=50, df=50-1=49, Mean1=4, SD1=3 1-sample 2-tail ttest Assume Null Hypothesis Ho as Mean1 = 4 Thus, Alternate Hypothesis Ha as Mean1 ≠ 4

  pythonhypothesis-testingone-sample-t-testscipystatsnormtwo-tail-testt-scorep-valuenull-hypothesisalternate-hypothesismeanstandard-deviation
  Language:Jupyter Notebook 1

• vaitybharati / P17.-Hypothesis-Testing-1-Sample-1-Tail-Test-Salmonella-Outbreak-

  Hypothesis-Testing-1-Sample-1-Tail-Test-Salmonella-Outbreak. 1-sample 1-tail ttest. Assume Null Hypothesis Ho as Mean Salmonella <= 0.3. Thus Alternate Hypothesis Ha as Mean Salmonella > 0.3. As No direct code for 1-sample 1-tail ttest available with unknown SD and arrays of means. Hence we find probability using 1-sample 2-tail ttest and divide it by 2 to get 1-tail ttest.

  pythonhypothesis-testingone-sample-t-testtwo-tail-testone-tail-testpandasnumpyscipystatsarrayp-valuesignificance-testingt-scorenull-hypothesisalternate-hypothesis
  Language:Jupyter Notebook 1

• ZL63388 / ab-testing-codes

  This repository is a collection of basic code templates for A/B Testing. All codes I am sharing are from the practical exercises I did from the Data Science Infinity Program.

  chi-square-testab-testingone-sample-t-testindependent-sample-t-testpaired-sample-t-test
  Language:Python 1

• ankitraj1602 / FundamentalOfDataAnalysisEdX

  R Projects relevant to two part Data Analysis Course. Link to Part 1 and 2 : https://courses.edx.org/courses/course-v1:UTAustinX+UT.7.20x+1T2016/e40867c6134a4731a492e1ab9c5027b3/

  corelation-of-featureshypothesis-testinglinear-modelslogistic-modelone-sample-t-testregressionsampling-distributiontwo-sample-t-test
  0

• NataliaVelasquez18 / Car_Data_Statistical_Analysis

  The purpose of this analysis is to help Mechacar's Manufacturing team to understand what car features impact car performance the most. The manufacturing team will incorporate the insights into the manufacturing process aiming to produce the best performing cars in the market, rebrand the company image, and regain market share.

  vehicle-performancehypothesis-testingrstudiodescriptive-statisticssummary-statisticsmultiple-linear-regressionone-sample-t-test
  Language:R 0

• NikhilaThota / Comparing_group_means

  Compare the means of two groups (male & female) body temparatures

  normal-distributioncentral-limit-theoremz-scoremeanvariancestandard-deviationone-sample-t-testhypothesis-testingp-valuef-statistics
  Language:Jupyter Notebook 0

• OGCJN / Tail-approximations-for-the-Student-t--F--and-Welch-statistics

  We present a detailed study of the asymptotic behavior of the distribution of the tails of these, perhaps, most commonly used statistical tests under non-standard conditions, that is, releasing the underlying assumptions of normality, independence and identical distribution and considering a more general case where one only assumes that the vector of data has a continuous joint density.

  one-sample-t-testtwo-sample-t-testf-testwelch-statisticsmall-sample-sizehigh-throughput-screeningt-teststatisticsextremevaluetheorysmallsamplesize
  Language:Mathematica
• Dunbar-Number-t-test

  pkhiyara / Dunbar-Number-t-test

  An R project that conducts a hypothesis test to analyze the validity of Robin Dunbar's claim that humans only have the capacity to keep track of a maximum of 150 people (Dunbar's Number) at a time. Through this project, Dunbar's hypothesis is investigated with the use of multiple t-tests conducted over a Facebook usage dataset.

  t-testt-testshypothesis-testingrfacebookhypothesis-teststwo-sample-t-testone-sample-t-testttest
  Language:R