实验二 描述性统计分析

实验二描述性统计分析一、实验目的学习利用SPSS进行描述性统计分析，编制变量数列。二、实验内容及实验步骤（一）探索分析（Explore过程）实验内容：下表为30名10岁少儿的身高（cm）资料，试作探索性分析。身高身高编号男孩女孩编号男孩女孩1121.4133.49128.2125.42131.5132.710137.4137.53132.6130.111135.5120.94129.2136.712129.0138.85134.1139.713132.2138.66135.8133.014140.9141.47140.4140.315129.3137.58136.0124.0实验步骤：建立数据文件。定义少儿身高的变量名为Height，然后再定义一个变量为SEX,用于作性别分组。顺序输入少儿身高的原始数据，在变量SEX中，男孩输入1、女孩输入2,并设置其值标签。2.选择菜单"AnalyzefDescriptiveStatisticsfExplore",弹出“Explore”对话框。现欲对少儿身高资料进行分组的探索性分析，故在对话框左侧的变量列表中选x，使之进入DependentList框，再选变量sex，使之进入FactorList框。单击"Statistics”按钮，弹出“Explore:Statistics”对话框，选中Descriptives、M-estimators、Outliers、Percentiles以及GroupedFrequencytables。单击"Continue”按钮返回“Explore"对话框。点击“Plot”按钮弹出“Explore:Plot”对话框，在Boxplot栏内选“Factorlevelstogether"，按组别进行箱图绘制；在“Descriptive”栏内选“Stem-and-leaf”，作茎叶情形描述。单击"Continue”按钮返回"Explore"对话框。单击“OK”按钮，得到输出结果。结果分析：描述统计量：分组显示男女各组的均值、置信区间、5%修正均值、中位数、方差、标准差、最小值、最大值、全距、四分位间距、偏度和峰度。

DescriptivesGenderStatisticStd.ErrorHeight0Mean134.00001.6428295%ConfidenceLowerBound130.4765InternalforMeanUpperBound137.52355%TrimmedMean134.3167Median136.7000Variance40.483Std.Deviation6.36261Minimum120.9Maximum141.4Range20.50InterquartileRange8.70Skewness-.894.580Kurtosis-.2751.1211Mean132.90001.3192095%ConfidenceLowerBound130.0706InternalforMeanUpperBound135.72945%TrimmedMean133.0944Median132.6000Variance26.104Std.Deviation5.10924Minimum121.4Maximum140.9Range19.50InterquartileRange6.80Skewness-.424.580Kurtosis.4961.121M估计M-EstimatorsHuber'sTukey'sHampel'sAndrew;'GenderM-EstimatoPBiweighPM-FstimatoFWav&Height0135.4183136.2104135.1852136.23271132.9127133.0901133.0153133.0904Theweightingconstantis1.339.Theweightingconstantis4.685.Theweightingconstantsare1.700,3.400,and8.500Theweightingconstantis1.340*pi.百分位数

PercentilesH八■ercenuiesccWeightedHeight0Average(Definition1)1120.9000—121.4000122.7600—125.4800130.1000—129.2000136.7000—132.6000138.8000—136.0000140.7400—140.6000.Tukey'sHingesHeight01131.40001292500136.70001326000138.700013590004.极值ExtremeValuesGenderCaseNumberValueHeight0Highest129141.4222140.3320139.7427138.8528138.6Lowest126120.9223124.0324125.4418130.1517132.71Highest114140.927140.4310137.448136.056135.8Lowest11121.429128.2312129.044129.2515129.35.正态性检验TestsofNormalityGenderKolmogorov-SmnovaShapiro-WilkStatisticdfSigStatisticdfSigHeight0.19815.119.89515.0791.11215.200*.96315.751*.Thisisalowerboundofthetruesignificance.a.LillieforsSignificanceCorrection由上可以看出，在0.05水平下不能拒绝正态性假设。6.QQ图NormalQ-QPlotcfHeight120125120125130135140145ObservedValue120125140NormalQ-QPlotfHeight130135120125140ObservedValue由上图可以看出，各组数据均在直线周围，因此可以认为他们服从正态分布。而且男性数据与直线拟合更好，说明更近似于正态分布，这一点从正态性检验结果5可以证实。（二）描述性分析（Descriptives过程）实验内容：调查20名男婴的出生体重（克）资料如下，试作描述性统计。27702915279529952860297030873126312546542272350334183921266942183707231025733881实验步骤：建立数据文件。激活数据管理窗口，定义男婴出生体重的变量名为X,然后输入男婴出生体重的原始数据。选择菜单"Analyze^DescriptiveStatistics^Descriptives"，弹出“Descriptives"对话框。在对话框左侧的变量列表中选x,进入“Variable（s）"框。并选中“Savestandardizedvalueasvariables"项。单击“OK”按钮，得到输出结果。（三）频数分析（Frequencies过程）实验内容：调查100名健康女大学生的血清总蛋白含量（g%）如下表，试作频数分析。7.437.886.887.807.048.056.977.127.358.057.957.567.507.887.207.207.207.437.127.207.507.357.887.437.586.507.437.126.976.807.357.507.206.437.588.036.977.437.357.357.587.586.887.657.047.128.127.507.046.807.047.207.657.437.657.766.737.207.507.437.357.957.357.476.507.658.167.547.277.276.727.657.277.047.726.886.736.736.737.277.587.357.507.277.357.357.278.167.037.437.357.957.047.657.277.728.437.507.657.04实验步骤：建立数据文件。定义血清总蛋白含量的变量名为X，然后输入血清总蛋白含量的原始数据.数据预分析选择菜单"Analyze^DescriptiveStatistics^Explore"，在弹出对话框中选择血清总蛋白含量进入DependentList；并在Display选项中选择plotsfplotsBoxplotsfNonefDescriptivefStem-andTeaffcontinue.根据茎叶图，观察数据是否分布均匀，从而确定是采用等距式分组还是异距式分组。根据经验公式确定组数以及组距，并确定适当组限。计算频数对原始数据进行重新编码，利用TransformfRecodefIntoDifferentVariables将该100名健康女大学生的血清总蛋白含量分组并赋值给新变量NewVar。选择菜单“AnalyzefDescriptiveStatisticsfFrequencies",弹出“Frequencies"对话框。在对话框左侧的变量列表中选择变量NewVar，使之进入“Variable(s)"框。选中复选框"Displayfrequencytables"，输出变量的频数分布表。3’单击“Statistics”按钮，弹出“Frequencies:Statistics”对话框，选择四分位数(Quartiles)、均数(Mean)、中位数(Median)、众数(Mode)、总和(Sum)、标准差(Std.deviation)、方差(Variance)、全距(Range)、最小值(Minimum)>最大值(Maximum)、标准误(S.E.mean)、偏度系数(Skewness)和峰度系数(Kurtosis)，单击“Continue"按钮返回。单击“Charts”钮，弹出“Frequencies:Charts”对话框。选择“Histograms"及“NormalCurve"，对变量x绘制直方图，并绘制正态曲线。单击"Continue”按钮返回。单击“OK”按钮，得到输出结果。结果分析：1.基本统计量DescriptivesStatisticStd斤ror血清总蛋白Mean7.3660.03940含量(g%)95%ConfidenceLowjrBound7.2878InternalforMeanUpperBound7.44425%TrimmedMean7.3644Median7.3500Variance.155Std.Deviation.39401Minimum6.43Maximum8.43Range2.00InterquartileRange.46Skewness.060.241Kurtosis.034.478茎叶图血清总蛋白含量(g%)Stem-and-LeafPlotFrequencyStem&Leaf1.001Extremes(=<6.43)2.0065.00.0066.5.0067.233335.0068.008883.0069.7778.0070.344444444.0071.222214.0072.0000000777777711.0073.555555555559.0074.333333337

14.0075.000000046888887.005555555TOC\o"1-5"\h\z3.0077.2264.0078.08883.0079.5553.0080.3553.0081.2661.00Extremes(>=8.43)Stemwidth:.10Eachleaf:case(s)3.盒子图8.508.007.507.006.506.00血清总蛋白含量（g%）7.00Stemwidth:.10Eachleaf:case(s)3.盒子图8.508.007.507.006.506.00由茎叶图和盒子图基本可以判断，该数据是均匀且对称的。可以利用等距式分组，组数G=K=1+lgn/lg2=7~8组，全距R=8.43-6.43=2.00，取G=8，则组距为I=R/G=0.25（取0.30）.采用两段开口式分组，最低组上限为6.70,……，最大组下限8.50.变量数列

血清蛋白分组FrequencyPercentValidPercentCumulativePercentValid131.53.03.02136.513.016.032613.026.042.043417.034.076.05147.014.090.06105.010.0100.0Total10050.0100.0MissingSystem10050.0Total200100.0实验：某医师测得血红蛋白值（g%）如表2.1,请编制血红蛋白质的变量数列，要求写出编制过程，并对结果进行必要解释。表2.1对象编号性别年龄血红蛋白值对象编号性别年龄血红蛋白值1女1812.8321女1611.362男1615.5022男1612.783女1812.2523男1815.094女1710.0624女188.675男1610.8825女178