Regression Interpertation

Regression Interpertation

• I need to know how to summrize the data as a whole

• Total voters
2
• Poll closed .

CREATIONS

New Member
I have worked on this for 4 days now and have completed all the regression analysis. The problem is i am lost and i need to interpret the results of my regression analysis, state the limitations of my analysis, and describe the significance of the results to the organization as a whole; I need some help on how this. Here is my data below:

Descriptive statistics American League

Salaries Wins
count 14 14
mean 75,479,911.4286 81.714
sample standard deviation 45,931,229.3816 13.070
population standard deviation 44,260,440.1103 12.595
confidence interval 95.% lower 48,960,009.1879 74.168
confidence interval 95.% upper 101,999,813.6692 89.261
p-value .0712 .2077

Descriptive statistics National League

Salaries Wins
count 16 16
mean 70,949,258.6250 80.375
sample standard deviation 20,668,652.9789 8.831
population standard deviation 20,012,337.1940 8.550
confidence interval 95.% lower 59,935,710.9262 75.669
confidence interval 95.% upper 81,962,806.3238 85.081
p-value .1353 .6456

Hypothesis Test: Independent Groups (z-test) Salaries

National League American League
75,479,911.4286 70,949,258.6250 mean
45,931,229.3816 20,668,652.9789 std. dev.
14 16 n

4,530,652.80357 difference (Group 1 - Group 2)
13,318,815.62859 standard error of difference
0 hypothesized difference

0.34 z
.7337 p-value (two-tailed)

-21,573,746.14520 confidence interval 95.% lower
30,635,051.75234 confidence interval 95.% upper
26,104,398.94877 half-width

Hypothesis Test: Independent Groups (z-test) Wins

National League American League
81.714 80.375 mean
13.070 8.831 std. dev.
14 16 n

1.3393 difference (Group 1 - Group 2)
4.1324 standard error of difference
0 hypothesized difference

0.32 z
.7459 p-value (two-tailed)

-6.7600 confidence interval 95.% lower
9.4386 confidence interval 95.% upper
8.0993 half-width

Randomized blocks ANOVA

Mean n Std. Dev
89.5 81.0000 30 10.8342 Wins
89.5 102.0000 30 11.4169 Errors
89.5 85.5000 30 32.7938 Stolen Bases

85.6667 3 27.3008 Block 1
93.0000 3 22.5389 Block 2
83.3333 3 30.0389 Block 3
72.6667 3 24.5832 Block 4
94.0000 3 10.1489 Block 5
83.0000 3 18.1934 Block 6
79.3333 3 13.6504 Block 7
96.6667 3 13.6504 Block 8
97.6667 3 15.0111 Block 9
78.3333 3 24.8261 Block 10
89.3333 3 3.0551 Block 11
81.6667 3 18.1475 Block 12
78.6667 3 10.0167 Block 13
94.3333 3 9.8150 Block 14
98.0000 3 15.6205 Block 15
114.0000 3 35.6791 Block 16
114.0000 3 42.8836 Block 17
78.0000 3 40.7308 Block 18
87.0000 3 22.6053 Block 19
82.3333 3 11.6762 Block 20
69.0000 3 32.9090 Block 21
84.6667 3 21.0792 Block 22
95.6667 3 10.9697 Block 23
82.3333 3 24.0901 Block 24
88.0000 3 17.0587 Block 25
110.0000 3 23.5160 Block 26
85.6667 3 16.5025 Block 27
114.3333 3 40.6120 Block 28
83.0000 3 33.6452 Block 29
91.3333 3 6.3509 Block 30
89.5000 90 22.6618 Total

ANOVA table
Source SS df MS F p-value
Treatments 7,335.000 2 3,667.5000 8.12 .0008
Blocks 12,162.500 29 419.3966 0.93 .5768
Error 26,209.000 58 451.8793
Total 45,706.500 89

Post hoc analysis
Tukey simultaneous comparison t-values (d.f. = 58)
Wins Stolen Bases Errors
81.0000 85.5000 102.0000
Wins 81.0000
Stolen Bases 85.5000 0.82
Errors 102.0000 3.83 3.01

critical values for experimentwise error rate:
0.05 2.40
0.01 3.04

p-values for pairwise t-tests
Win Stolen Bases Errors
81.0000 85.5000 102.0000
Wins 81.0000
Stolen Bases 85.5000 .4156
Errors 102.0000 .0003 .0039

Chi-square Variance Test

1.0000 hypothesized variance
117.3793 observed variance of Data
30 n
29 df
3404.00 chi-square
0.00E+00 p-value (two-tailed)

Chi-square Variance Test

1.000 hypothesized variance
130.345 observed variance of Data
30 n
29 df
3780.00 chi-square
0.00E+00 p-value (two-tailed)

Chi-square Variance Test

1.000 hypothesized variance
1,075.431 observed variance of Data
30 n
29 df
31187.50 chi-square
0.00E+00 p-value (two-tailed)

One factor ANOVA

Mean n Std. Dev
93.75 102.0 30 11.42 Errors
93.75 85.5 30 32.79 Stolen Bases
93.8 60 25.73 Total

ANOVA table
Source SS df MS F p-value
Treatment 4,083.75 1 4,083.750 6.77 .0117
Error 34,967.50 58 602.888
Total 39,051.25 59

Hypothesis Test: Independent Groups (z-test)

Errors Stolen Bases
102.00 85.50 mean
11.42 32.79 std. dev.
30 30 n

16.500 difference (Group 1 - Group 2)
6.340 standard error of difference
0 hypothesized difference

2.60 z
.0093 p-value (two-tailed)

F-test for equality of variance
1,075.43 variance: Group 2
130.34 variance: Group 1
8.25 F
1.84E-07 p-value

Correlation Matrix

National League Batting Avg Wins
Batting Avg 1.000
Wins .318 1.000

16 sample size

± .497 critical value .05 (two-tail)
± .623 critical value .01 (two-tail)

Correlation Matrix

American League Batting Avg Wins
Batting Avg 1.000
Wins .285 1.000

14 sample size

± .532 critical value .05 (two-tail)
± .661 critical value .01 (two-tail)

Regression Analysis
National League
r² 0.101 n 16
r 0.318 k 1
Std. Error 8.668 Dep. Var. Y

ANOVA table
Source SS df MS F p-value
Regression 117.9350 1 117.9350 1.57 .2308
Residual 1,051.8150 14 75.1296
Total 1,169.7500 15

Regression output confidence interval
variables coefficients std. error t (df=14) p-value 95% lower 95% upper
Intercept -32.1309 89.8227 -0.358 .7259 -224.7814 160.5196
X1 430.0274 343.2262 1.253 .2308 -306.1195 1,166.1744

Regression Analysis
American League
r² 0.081 n 14
r 0.285 k 1
Std. Error 13.041 Dep. Var. Y

ANOVA table
Source SS df MS F p-value
Regression 180.0346 1 180.0346 1.06 .3238
Residual 2,040.8226 12 170.0685
Total 2,220.8571 13

Regression output confidence interval
variables coefficients std. error t (df=12) p-value 95% lower 95% upper
Intercept -61.4499 139.1889 -0.441 .6667 -364.7164 241.8167
X1 534.9075 519.8915 1.029 .3238 -597.8388 1,667.6537