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Catégorie :Category: nCreator TI-Nspire
Auteur Author: SamSamDaBoss
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 4.24 Ko KB
Mis en ligne Uploaded: 28/04/2025 - 07:57:08
Uploadeur Uploader: SamSamDaBoss (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4606315
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 4.24 Ko KB
Mis en ligne Uploaded: 28/04/2025 - 07:57:08
Uploadeur Uploader: SamSamDaBoss (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4606315
Description
Fichier Nspire généré sur TI-Planet.org.
Compatible OS 3.0 et ultérieurs.
<<
Describing/Comparing Distributions (CUSS): C enter: Median or Mean U nusual features: Gaps or Outliers S pread: Interquartile Range (IQR), Standard Deviation, or Range S hape: Symmetrical, Bimodal, or Skewed Binomial Distribution (BINS): B inary: Success or Failure I ndependent trials N umber of trials is fixed S uccess probability stays the same Defining a Binomial Distribution: "X has a binomial distribution with p = ___ and n = ___" NSpire steps: Menu 6: Stats 5: Distributions A or B Geometric Distribution (BITS): B inary: Success or Failure I ndependent trials T rials until first success S uccess probability stays the same Defining a Geometric Distribution: "X has a geometric distribution with p = ___" NSpire steps: Menu 6: Stats 5: Distributions H or I Constructing a Confidence Interval (PANIC): P arameters: Define, including confidence level A ssumptions/Conditions: Check assumptions N ame the interval I nterval calculation C onclusion in context 1-Proportion Z Interval/Test: Assumptions: Random: Sample must be randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR np e 10 and n(1-p) e 10 NSpire steps: Interval: Menu 6: Stats 6: CI 5: 1-Prop Z Interval Test: Menu 6: Stats 7: Stats Tests 5: 1-Prop Z Test 2-Proportion Z Interval/Test: Assumptions (CHECK BOTH SAMPLES): Random: Both samples randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR np e 10 and n(1-p) e 10 NSpire steps: Interval: Menu 6: Stats 6: CI 6: 2-Prop Z Interval Test: Menu 6: Stats 7: Stats Tests 6: 2-Prop Z Test 1-Sample t-Interval/Test: Assumptions: Random: Sample randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR no unusual features in graph OR n e 30 Interval NSpire steps: Menu 6: Stats 6: CI 2: t Interval Test NSpire steps: Menu 6: Stats 7: Stats Tests 2: t Test Formulas: Interval: mean ± t* (s/n) Test statistic: t = (sample mean - population mean) / (s/n) invT for Critical Value: Menu Statistics Distribution Inverse T 2-Sample t-Interval/Test: Assumptions (CHECK BOTH SAMPLES): Random: Samples randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR no unusual features OR n e 30 Interval NSpire steps: Menu 6: Stats 6: CI 4: 2-Sample t Interval Test NSpire steps: Menu 6: Stats 7: Stats Tests 4: 2-Sample t Test Notes: DOUBLE your p-value if doing a two-tailed test Use z-tests for proportions, t-tests for means Given/Conditional Probability: P(A|B) = P(A and B) / P(B) If A and B are Independent: P(A and B) = P(A) × P(B) Errors: Type I Error (±): Rejecting a true null (false positive) Type II Error (²): Failing to reject a false null (false negative) Changing Error Rates: Increasing ± Decreases ² Decreasing ± Increases ² Conclusions from Hypothesis Testing: If p > ±: Fail to Reject H No convincing evidence for Ha If p < ±: Reject H Convincing evidence for Ha Effect of Outliers: Mean and Standard Deviation affected Median and IQR resistant Formula: z = (x - ¼)/ Ã Interpretation: How many standard deviations from the mean Empirical Rule: (68-95-99.7 Rule) Sampling Methods Simple Random Sample (SRS) Description : Each unit (or set of units) has an equal chance of being selected. Examples : names in a hat, random number generator/table. Pros : Easy and unbiased. Cons : Large variation; must know the population. Stratified Random Sample Description : Divide population into groups (strata) based on a similar characteristic; take an SRS from each group. Pros : More precise than SRS; can be cheaper if groups already exist. Cons : Difficult to divide into groups; must know the population. Cluster Sampling Description : Divide population into groups (often by location); randomly select a group and sample everything in that group. Pros : Cost is reduced; unbiased; no need to know the full population. Cons : May not represent the overall population. Systematic Sampling Description : Use a system (like every nth number) after randomly choosing where to begin. Pros : Unbiased; sample evenly distributed. Cons : Large variation; trends could affect results. Voluntary Sampling Description : People choose whether or not to participate. Pros : Easy. Cons : Highly unrepresentative of the population. Convenience Sampling Description : Sample people who are easy to reach. Pros : Easy. Cons : Highly unrepresentative of the population. Bias in Sampling Voluntary Response Bias : People choose themselves to participate. Undercoverage Bias : Some groups are left out of the sample selection process. Non-response Bias : Someone cannot or does not participate in the sample. Response Bias : False or incorrect answers (either intentional or not). Wording of Questions Bias : Wording is slanted to favor a certain response. General Vocabulary Observational Study : Treatment is NOT randomly assigned. Experiment : Trea
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Compatible OS 3.0 et ultérieurs.
<<
Describing/Comparing Distributions (CUSS): C enter: Median or Mean U nusual features: Gaps or Outliers S pread: Interquartile Range (IQR), Standard Deviation, or Range S hape: Symmetrical, Bimodal, or Skewed Binomial Distribution (BINS): B inary: Success or Failure I ndependent trials N umber of trials is fixed S uccess probability stays the same Defining a Binomial Distribution: "X has a binomial distribution with p = ___ and n = ___" NSpire steps: Menu 6: Stats 5: Distributions A or B Geometric Distribution (BITS): B inary: Success or Failure I ndependent trials T rials until first success S uccess probability stays the same Defining a Geometric Distribution: "X has a geometric distribution with p = ___" NSpire steps: Menu 6: Stats 5: Distributions H or I Constructing a Confidence Interval (PANIC): P arameters: Define, including confidence level A ssumptions/Conditions: Check assumptions N ame the interval I nterval calculation C onclusion in context 1-Proportion Z Interval/Test: Assumptions: Random: Sample must be randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR np e 10 and n(1-p) e 10 NSpire steps: Interval: Menu 6: Stats 6: CI 5: 1-Prop Z Interval Test: Menu 6: Stats 7: Stats Tests 5: 1-Prop Z Test 2-Proportion Z Interval/Test: Assumptions (CHECK BOTH SAMPLES): Random: Both samples randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR np e 10 and n(1-p) e 10 NSpire steps: Interval: Menu 6: Stats 6: CI 6: 2-Prop Z Interval Test: Menu 6: Stats 7: Stats Tests 6: 2-Prop Z Test 1-Sample t-Interval/Test: Assumptions: Random: Sample randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR no unusual features in graph OR n e 30 Interval NSpire steps: Menu 6: Stats 6: CI 2: t Interval Test NSpire steps: Menu 6: Stats 7: Stats Tests 2: t Test Formulas: Interval: mean ± t* (s/n) Test statistic: t = (sample mean - population mean) / (s/n) invT for Critical Value: Menu Statistics Distribution Inverse T 2-Sample t-Interval/Test: Assumptions (CHECK BOTH SAMPLES): Random: Samples randomly selected/assigned Independence: Sample size < 10% of population Normality: Stated OR no unusual features OR n e 30 Interval NSpire steps: Menu 6: Stats 6: CI 4: 2-Sample t Interval Test NSpire steps: Menu 6: Stats 7: Stats Tests 4: 2-Sample t Test Notes: DOUBLE your p-value if doing a two-tailed test Use z-tests for proportions, t-tests for means Given/Conditional Probability: P(A|B) = P(A and B) / P(B) If A and B are Independent: P(A and B) = P(A) × P(B) Errors: Type I Error (±): Rejecting a true null (false positive) Type II Error (²): Failing to reject a false null (false negative) Changing Error Rates: Increasing ± Decreases ² Decreasing ± Increases ² Conclusions from Hypothesis Testing: If p > ±: Fail to Reject H No convincing evidence for Ha If p < ±: Reject H Convincing evidence for Ha Effect of Outliers: Mean and Standard Deviation affected Median and IQR resistant Formula: z = (x - ¼)/ Ã Interpretation: How many standard deviations from the mean Empirical Rule: (68-95-99.7 Rule) Sampling Methods Simple Random Sample (SRS) Description : Each unit (or set of units) has an equal chance of being selected. Examples : names in a hat, random number generator/table. Pros : Easy and unbiased. Cons : Large variation; must know the population. Stratified Random Sample Description : Divide population into groups (strata) based on a similar characteristic; take an SRS from each group. Pros : More precise than SRS; can be cheaper if groups already exist. Cons : Difficult to divide into groups; must know the population. Cluster Sampling Description : Divide population into groups (often by location); randomly select a group and sample everything in that group. Pros : Cost is reduced; unbiased; no need to know the full population. Cons : May not represent the overall population. Systematic Sampling Description : Use a system (like every nth number) after randomly choosing where to begin. Pros : Unbiased; sample evenly distributed. Cons : Large variation; trends could affect results. Voluntary Sampling Description : People choose whether or not to participate. Pros : Easy. Cons : Highly unrepresentative of the population. Convenience Sampling Description : Sample people who are easy to reach. Pros : Easy. Cons : Highly unrepresentative of the population. Bias in Sampling Voluntary Response Bias : People choose themselves to participate. Undercoverage Bias : Some groups are left out of the sample selection process. Non-response Bias : Someone cannot or does not participate in the sample. Response Bias : False or incorrect answers (either intentional or not). Wording of Questions Bias : Wording is slanted to favor a certain response. General Vocabulary Observational Study : Treatment is NOT randomly assigned. Experiment : Trea
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