数据库课程设计结论

In this article, we will learn about different types[Z Test and t Test] of commonly used Hypothesis Testing.

在本文中，我们将学习常用假设检验的不同类型[ Z检验和t检验 ]。

假设是什么？ (What is Hypothesis?)

This is a Statistical process which is an assumption about population parameter.

这是一个统计过程，是有关总体参数的假设。

Using the hypothesis testing we can reject / accept the assumptions made by projecting the data from Sample to Population (or) from Population to Sample.

使用假设检验，我们可以拒绝/接受通过从样本到总体(或从总体到样本)的数据投影所做的假设。

This process can also be termed as validity of projection.This operates around Null Hypothesis H0 & Alternative Hypothesis H1.

此过程也可以称为投影的有效性。此过程围绕零假设 H0和替代假设H1进行 。

There are few commonly used Tests which can be classified based on 2 categories:

很少有可以根据2类进行分类的常用测试：

Sampling distribution Of Means — Z Test & T Test

均值的抽样分布-Z检验和T检验

Sampling distribution Of Variance — Chi squared Test & F Test

方差的抽样分布-卡方检验和F检验

Z检验： (Z Test:)

Assumptions for Z Test:

Z测试的假设：

a. Sample size should be greater than 30

一个。样本数量应大于30

b. Population Standard Deviation should be known

b。人口标准偏差应该是已知的

c. Variables in data should be continuous

C。数据变量应该是连续的

Steps for Z Test:

Z测试步骤：

a. State H0 or H1 — [From the given problem we need to find]

一个。状态H0或H1-[根据给定的问题，我们需要找到]

b. Choose the level of significance — [Will be given in problem statement] If it is 0.05 ->1–0.05 = 95%

b。选择显着性水平-[将在问题陈述中给出]如果为0.05-> 1–0.05 = 95％

c. Find the Critical values — Range for 95% -> Refer the Z score table → -1.96 to +1.96

C。查找临界值-范围为95％->请参阅Z得分表→-1.96至+1.96

From Emperical split there are 3 most widely used values of Z we can directly take depending on the value:

从Emperical split中可以得出3个最常用的Z值，具体取决于该值：

If the Confidence Level is 99% — Z score value is 2.56, 95% — 1.96,90% — 1.64

如果置信度为99％ -Z得分值为 2.56，则95％-1.96,90％-1.64

d. Find the Test Statistics — Z value using the below formula

d。使用以下公式找到“测试统计量”-Z值

e. Arrive at a Conclusion, to accept the hypothesis or reject the hypothesis

e。得出结论，接受假设或拒绝假设

Image for post — “Z” calculation formula

Where the variables are;

变量在哪里；

X =Mean of the Sample,
X =样本均值，
µ =Mean of population
µ =人口平均值
σ = Standard Deviation of population
σ=总体标准差
n = No. of observations
n =观察数

If the Z value falls within the Critical Value range, then we can accept the Hypothesis, else it has to be rejected

如果Z值在临界值范围内，则我们可以接受假设，否则就必须拒绝该假设

If the Confidence level is other than the above 3 values, then we need to use Z Score table to find the Z Scores/probability value, with which we can decide on accepting or rejecting the hypothesis.

如果置信度水平不是上述3个值，则需要使用Z分数表查找Z分数/概率值，我们可以使用该值决定接受还是拒绝该假设。

If the resultant value[Test Statistics — Z Value] is negative, then we need to verify negative Z score table. Else we need to verify positive Z score table.

如果结果值[测试统计数据-Z值]为负，则需要验证负Z得分表 。否则我们需要验证正Z得分表 。

Sample 1: If Test Statistic Z value = 1.26 , then we need to use positive Z score table. Where 1.2 in Y axis and 0.06 in X axis.

样本1：如果测试统计Z值= 1.26，那么我们需要使用正Z得分表。其中Y轴为1.2，X轴为0.06。

Sample 2: If the Test statistic value is negative, we need to refer the negative Z score table. Finally to get the actual area / probability we need to subtract the Z score value from 1.

示例2：如果“测试”统计量值为负，则需要参考负Z得分表。最后，要获得实际面积/概率，我们需要从1中减去Z得分值。