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megans math

R Software Module: esteq.wasp (opens new window with default values)

Title produced by software: Estimate Equation

Date of computation: Fri, 25 Apr 2008 13:19:17 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Apr/25/t1209151597aegq0cdfaovar9u.htm/, Retrieved Fri, 25 Apr 2008 21:26:37 +0200

User-defined keywords:

megsteroo

Dataseries X:

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660 39 930 59 710 33 970 52 660 34 720 45 1010 68 1140 85 1130 66 490 37 1070 76 1030 74 470 17 460 17 680 40 770 48 920 58 1010 66 1160 76 150 84 370 21 420 25 290 12 340 16 420 22 510 29 620 39 820 55 1010 70 950 59 970 55 250 9 300 12 440 23 410 19 510 26 740 42 540 29 460 24 510 28

Text written by user:

Output produced by software:

This free online calculator computes equations with the following options: constant, linear deterministic trend, first differences, hyperbolic, exponential, geometric, quadratic, cubic, quartic, seasonal dummies, predetermination (lagged endogenous variables), Ordinary Least Squares, Bootstrap, Jackknife.

Econometric Regression Equation

Multiple Linear Regression - Estimated Regression Equation

Calories[t] = +10.236144286881 Fat(g)[t] +243.27880748647 + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.E.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
Fat(g)[t]	10.236144	1.275036	8.028119	0	0
Constant	243.278807	60.592745	4.014982	0.00027	0.000135

Variable	Elasticity	S.E.^*	T-STAT H0: \|elast\| = 1	2-tail p-value	1-tail p-value
%Fat(g)[t]	0.639854	0.079702	-4.518684	5.9E-5	2.9E-5
%Constant	0.360146	0.089701	-7.133217	0	0
Variable	Stand. Coeff.	S.E.^*	T-STAT H0: coeff = 0	2-tail p-value	1-tail p-value
S-Fat(g)[t]	0.793152	0.098797	8.028119	0	0
S-Constant	0	0	0	1	0.5
^*Note	computed against deterministic endogenous series
Variable	Partial Correlation
Fat(g)[t]	0.793152
Constant	0.545763
Critical Values (alpha = 5%)
1-tail CV at 5%	1.69
2-tail CV at 5%	2.02

Multiple Linear Regression - Regression Statistics
Multiple R	0.793152
R-squared	0.62909
Adjusted R-squared	0.619329
F-TEST	64.450694
Observations	40
Degrees of Freedom	38
Multiple Linear Regression - Residual Statistics
Standard Error	175.830139
Sum Squared Errors	1174817.035044
Log Likelihood	-262.512412
Durbin-Watson	1.914131
Von Neumann Ratio	1.963211
# e[t] > 0	24
# e[t] < 0	16
# Runs	8
Stand. Normal Runs Statistic	-4.075734

Multiple Linear Regression - Ad Hoc Selection Test Statistics
Akaike (1969) Final Prediction Error	32462.049653
Akaike (1973) Log Information Criterion	10.387744
Akaike (1974) Information Criterion	32459.34053
Schwarz (1978) Log Criterion	10.472187
Schwarz (1978) Criterion	35319.394001
Craven-Wahba (1979) Generalized Cross Validation	32543.408173
Hannan-Quinn (1979) Criterion	33465.682794
Rice (1984) Criterion	32633.806529
Shibata (1981) Criterion	32307.468464

Multiple Linear Regression - Analysis of Variance
ANOVA	DF	Sum of Squares	Mean Square
Regression	1	1992572.964956	1992572.964956
Residual	38	1174817.035044	30916.237764
Total	39	3167390	81215.128205128
F-TEST	64.450694
p-value	0

Charts produced by software:

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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