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  • 10.9: Chapter Formula Review
    https://stats.libretexts.org/Courses/Fresno_City_College/Book%3A_Business_Statistics_Customized_(OpenStax)/10%3A_Hypothesis_Testing_with_Two_Samples/10.09%3A_Chapter_Formula_Review
    10.1 Comparing Two Independent Population Means Standard error: SE=√(s1)2n1+(s2)2n2 Test statistic (t-score): \(t_{c}=\frac{\left(\...10.1 Comparing Two Independent Population Means Standard error: SE=√(s1)2n1+(s2)2n2 Test statistic (t-score): tc=(¯x1−¯x2)−δ0√(s1)2n1+(s2)2n2 Degrees of freedom: \(d f=\frac{\left(\frac{\left(s_{1}\right)^{2}}{n_{1}}+\frac{\left(s_{2}\right)^{2}}{n_{2}}\right)^{2}}{\left(\frac{1}{n_{1}-1}\right)\left(\fr…
  • 10.10: Chapter Formula Review
    https://stats.libretexts.org/Courses/Fresno_City_College/Introduction_to_Business_Statistics_-_OER_-_Spring_2023/10%3A_Hypothesis_Testing_with_Two_Samples/10.10%3A_Chapter_Formula_Review
    10.1 Comparing Two Independent Population Means Standard error: SE=√(s1)2n1+(s2)2n2 Test statistic (t-score): \(t_{c}=\frac{\left(\...10.1 Comparing Two Independent Population Means Standard error: SE=√(s1)2n1+(s2)2n2 Test statistic (t-score): tc=(¯x1−¯x2)−δ0√(s1)2n1+(s2)2n2 Degrees of freedom: \(d f=\frac{\left(\frac{\left(s_{1}\right)^{2}}{n_{1}}+\frac{\left(s_{2}\right)^{2}}{n_{2}}\right)^{2}}{\left(\frac{1}{n_{1}-1}\right)\left(\fr…

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