ANGGRAENI PURWANINGSIH
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This document summarizes the results of a regression analysis examining the relationship between three variables: mathematics, physics, and chemistry test scores. The regression found a moderate negative correlation between mathematics and physics scores, but little correlation between mathematics and chemistry scores. The regression model explains about 19% of the variance in mathematics scores. Physics scores were a statistically significant predictor of mathematics scores in the model, while chemistry scores were not.
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Problem:
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Goal:
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Finding/Conclusion:
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Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 2933 2933.1 21.33 0.000
SBP 1 2933 2933.1 21.33 0.000
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Lack-of-Fit 21 2849 135.7 0.95 0.575
Pure Error 7 1002 143.1
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S R-sq R-sq(adj) R-sq(pred)
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Constant -18.3 13.9 -1.32 0.198
SBP 0.4454 0.0964 4.62 0.000 1.00
Regression Equation
Age = -18.3 + 0.4454 SBP
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Regression Analysis: Age versus SBP
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 1 4828.5 4828.47 66.81 0.000
SBP 1 4828.5 4828.47 66.81 0.000
Error 27 1951.4 72.27
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Model Summary
S R-sq R-sq(adj) R-sq(pred)
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The document presents a statistical analysis of measurement data from samples of an aircraft engine component to analyze variation in outer diameter. 25 samples were measured and their averages (X-bar) and ranges (R) were calculated. Control charts were created for X-bar and R, which identified 3 outliers in the R chart. After removing the outliers, revised control limits and charts were made. Process capabilities and indices were determined, showing the process is capable of producing conforming parts. The histogram suggested the process follows a normal distribution, and skewness/kurtosis analysis showed the population is normally distributed.
KalmanForecast
This document discusses forecasting daily streamflow in two Kenyan watersheds, Yala and Sondu, using the Kalman filter. Concurrent daily rainfall and streamflow data from 1972-1981 were used to identify transfer function models relating rainfall to streamflow. The Kalman filter was applied to dynamically update the model parameters based on new observations. Results showed the transfer function models adequately described the rainfall-runoff relationship and recursive simulations using the Kalman filter reproduced the temporal variations and mass balance of observed streamflows. However, the models tended to underestimate flows during extreme events.
Stanford 2009
1) The document describes using experimental design techniques to improve mineral resource evaluation in the early stages of exploration by testing different semivariogram parameters.
2) A case study of a lateritic nickel deposit was modeled by simulating grades on a 3D grid and calculating resources using different semivariogram parameters.
3) Results showed that using designed averaged kriging (DAK) with an optimal semivariogram parameter set improved the resource estimation by almost 10% compared to using the geologist's initial parameters.
B h curve-1
This document describes an experiment to study the magnetic properties of materials like iron. It provides definitions and descriptions of various equipment used in the experiment like a rheostat, magnetometer compass, electromagnetic coil, ammeter, and voltmeter. It also includes diagrams of the B-H curve and tables showing magnetic field intensity (H) and magnetic flux density (B) values measured during the experiment.
Regression analysis
The document appears to be an analysis of a dataset containing consumption, income, and liquid asset values for multiple observations.
(i) A regression model was developed relating consumption to income and liquid assets. High R-squared and variance inflation factors indicate potential multicollinearity between the predictors.
(ii) Additional analyses excluding variables one at a time were performed, continuing to show high correlation between predictors and potential multicollinearity issues.
(iii) A Theil's inequality coefficient calculation further supported the existence of multicollinearity within the dataset.
Erning spss baru sekali
SKRIPSI
PENGARUH BIAYA, LOKASI, FASILITAS DAN GROUP ACUAN TERHADAP PILIHAN SISWA PADA MTS MA’ARIF 03 SOJOKERTO Kabupaten Wonosobo
Viktor Urumov - Time-delay feedback control of nonlinear oscillators
Lecture by prof. dr Viktor Urumov (Faculty of Science and Mathematics, Saint Cyril and Methodius University, Skopje, Macedonia) on June 30, 2010 at the Faculty of Science and Mathematics, Nis, Serbia.
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- 1. NAMA : ANGGRAENI.P KELAS : 3D NIM : 082445 STATISTIK REGRESI TABEL DATA REGRESI PENGAMATAN TENTANG ADANYA HUBUNGAN DARI KE TIGA NILAI (KIMIA,MATEMATIKA,FISIKA)
- 2. KIMIA MATEMATIKA FISIKA 65 70 60 60 85 70 50 78 60 64 83 50 70 85 70 75 60 65 80 79 70 70 75 70 75 80 75 60 76 75 65 77 70 78 75 75 75 70 70 71 80 60 76 80 75 60 75 70 70 80 65 66 75 68 70 70 70 65 72 75 55 80 70 65 80 66 60 73 65 70 72 65 75 70 70 70 88 80 65 85 75 55 70 76 60 70 65 75 85 65
- 3. Regression Descriptive Statistics Mean Std. Deviation N MATAMATIKA 73.9333 7.01689 30 FISIKA 68.3333 6.08182 30 KIMIA 67.1667 7.45600 30 Correlations MATAMATIKA FISIKA KIMIA Pearson Correlation MATAMATIKA 1.000 -.427 -.147 FISIKA -.427 1.000 .107 KIMIA -.147 .107 1.000 Sig. (1-tailed) MATAMATIKA . .009 .218 FISIKA .009 . .286 KIMIA .218 .286 . N MATAMATIKA 30 30 30 FISIKA 30 30 30 KIMIA 30 30 30
- 4. Variables Entered/Removed b Model Variables Entered Variables Removed Method 1 KIMIA, FISIKA a . Enter a. All requested variables entered. b. Dependent Variable: MATAMATIKA Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .439 a .193 .133 6.53409 2.592 a. Predictors: (Constant), KIMIA, FISIKA b. Dependent Variable: MATAMATIKA ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 275.119 2 137.560 3.222 .056 a Residual 1152.747 27 42.694 Total 1427.867 29 a. Predictors: (Constant), KIMIA, FISIKA b. Dependent Variable: MATAMATIKA
- 5. Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 113.213 16.671 6.791 .000 FISIKA -.480 .201 -.416 -2.391 .024 .988 1.012 KIMIA -.097 .164 -.103 -.591 .560 .988 1.012 a. Dependent Variable: MATAMATIKA Collinearity Diagnostics a Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) FISIKA KIMIA 1 1 2.988 1.000 .00 .00 .00 2 .009 18.277 .02 .30 .79 3 .003 30.049 .98 .70 .21 a. Dependent Variable: MATAMATIKA
- 6. Charts