The document contains 6 tables summarizing the results of regressions analyzing factors that influence CO2 emissions:
Table 1.1 shows a regression of logCO2 on log population and log vehicle density. Tables 1.2-1.6 report additional results including mean error terms, correlation of error terms and variables, variance inflation factors, heteroskedasticity and normality tests.
Table 2.1 expands the regression to include an indicator for countries with high GDP. Tables 2.2-2.6 report similar additional results as the first model.
Table 3.1 includes dummy variables for regions. Tables 3.2-3.6 report test results.
Table 4.1 further expands
This document summarizes the time series analysis and forecasting model for Turkey's Consumer Price Index (CPI). It presents the results of stationarity tests, correlation analyses, and unit root tests on the CPI and other variables. It then estimates a linear regression model using first differences of the log-transformed time series to forecast CPI. Diagnostic tests show the model has no autocorrelation or heteroskedasticity and the residuals are normally distributed. The model provides statistically significant short-term forecasts of inflation.
This document provides instructions and examples for using the Casio fx-82SX/fx-250HC calculator. It covers basic calculations, constants, memory, fractions, percentages, scientific functions, statistics, and conversions. The examples show how to perform calculations in various modes, use special functions like logarithms and trigonometry, work with fractions and decimals, generate random numbers, and convert between units and coordinate systems.
This document provides calculations for determining the specifications of compression springs. It analyzes music wire, phosphor bronze, and stainless steel springs given various dimensional parameters. Equations are used to calculate properties like spring rate, shear stress, yield point, and critical buckling length. The summaries indicate some designs are not solid-safe due to exceeding the shear yield strength, and suggest adjusting the free length to achieve a solid-safe design.
This document describes research on modeling and optimizing the dynamics and gait of multi-link swimming robots using a "perfect fluid" model. The researchers formulated the dynamics of an articulated multi-link swimming robot moving in a planar environment. They reduced the system to first-order equations and developed simulations to examine performance for harmonic inputs and optimize displacement through inputs. Experiments were also planned using prototype robotic swimmers to compare with the theoretical model.
This document summarizes the results of fitting vector time series regression models to economic indicator data using SAS and R software. It estimates VARX(0,0), VARX(1,0), and VARX(1,2) models and compares the parameter estimates between the two programs. The VARX(0,0) model estimates showed good agreement between SAS and R. The VARX(1,0) model was also estimated using both programs, with SAS using least squares and R using the vars package. Parameter estimates were provided for the VARX(1,0) model.
This document provides calculations to determine the power rating of a gear set based on bending and wear criteria. It first calculates velocity, geometry, and load factors. It then determines the bending stress and torque on the pinion, finding a power rating of 4.54 hp. It next calculates the contact stress and torque for both gears based on wear, determining a power rating of 3.27 hp is controlled by the pinion. Therefore, the overall power rating of the gear set based on both bending and wear is 3.27 hp.
The document discusses bottom tetraquarks, including their current status and prospects. It provides an overview of bottomonium-like states and enigmatic decays of the Υ(5S). It also examines the spectroscopy of tetraquark states and charged tetraquarks. Finally, it summarizes recent results on the Yb(10890) and prospects for studying bottom tetraquarks.
This document summarizes the time series analysis and forecasting model for Turkey's Consumer Price Index (CPI). It presents the results of stationarity tests, correlation analyses, and unit root tests on the CPI and other variables. It then estimates a linear regression model using first differences of the log-transformed time series to forecast CPI. Diagnostic tests show the model has no autocorrelation or heteroskedasticity and the residuals are normally distributed. The model provides statistically significant short-term forecasts of inflation.
This document provides instructions and examples for using the Casio fx-82SX/fx-250HC calculator. It covers basic calculations, constants, memory, fractions, percentages, scientific functions, statistics, and conversions. The examples show how to perform calculations in various modes, use special functions like logarithms and trigonometry, work with fractions and decimals, generate random numbers, and convert between units and coordinate systems.
This document provides calculations for determining the specifications of compression springs. It analyzes music wire, phosphor bronze, and stainless steel springs given various dimensional parameters. Equations are used to calculate properties like spring rate, shear stress, yield point, and critical buckling length. The summaries indicate some designs are not solid-safe due to exceeding the shear yield strength, and suggest adjusting the free length to achieve a solid-safe design.
This document describes research on modeling and optimizing the dynamics and gait of multi-link swimming robots using a "perfect fluid" model. The researchers formulated the dynamics of an articulated multi-link swimming robot moving in a planar environment. They reduced the system to first-order equations and developed simulations to examine performance for harmonic inputs and optimize displacement through inputs. Experiments were also planned using prototype robotic swimmers to compare with the theoretical model.
This document summarizes the results of fitting vector time series regression models to economic indicator data using SAS and R software. It estimates VARX(0,0), VARX(1,0), and VARX(1,2) models and compares the parameter estimates between the two programs. The VARX(0,0) model estimates showed good agreement between SAS and R. The VARX(1,0) model was also estimated using both programs, with SAS using least squares and R using the vars package. Parameter estimates were provided for the VARX(1,0) model.
This document provides calculations to determine the power rating of a gear set based on bending and wear criteria. It first calculates velocity, geometry, and load factors. It then determines the bending stress and torque on the pinion, finding a power rating of 4.54 hp. It next calculates the contact stress and torque for both gears based on wear, determining a power rating of 3.27 hp is controlled by the pinion. Therefore, the overall power rating of the gear set based on both bending and wear is 3.27 hp.
The document discusses bottom tetraquarks, including their current status and prospects. It provides an overview of bottomonium-like states and enigmatic decays of the Υ(5S). It also examines the spectroscopy of tetraquark states and charged tetraquarks. Finally, it summarizes recent results on the Yb(10890) and prospects for studying bottom tetraquarks.
Uji BNT (Beda Nyata Terkecil) digunakan untuk membandingkan perbedaan rata-rata perlakuan. Uji ini menentukan nilai kritis untuk membandingkan selisih rata-rata antar perlakuan dan menentukan apakah perbedaan tersebut nyata secara statistik. Contoh penggunaan uji BNT untuk menguji pengaruh beberapa sistem olah tanah terhadap hasil kentang menunjukkan sistem olah tanah A memberikan hasil tertinggi sedang
Contoh soal spss independent dan one way anovapika setiawan
Berdasarkan hasil pengujian statistik independen sample t-test dan one-way ANOVA, terdapat perbedaan rata-rata pengetahuan komputer mahasiswa AKMI dan UNBARA serta minat calon mahasiswa antara sekolah kesehatan Alma'arif, Fatiah, dan Politeknik di Baturaja. H0 ditolak pada kedua uji statistik yang menunjukkan adanya perbedaan signifikan.
12 kul dan-responsi-uji-lanjut-multiple-comparison-testsardillah15
Dokumen tersebut membahas pengaruh konsentrasi bahan pengembang (foaming agent) terhadap overrun es krim. Ada empat konsentrasi yang diuji yaitu 0, 100, 200, dan 300 ppm. Jika rancangan yang digunakan adalah RAL, model liniernya adalah Yij = μ + αi + εij, dengan Yij adalah overrun es krim, μ adalah rata-rata, αi adalah pengaruh konsentrasi ke-i, dan εij adalah gal
Dokumen tersebut membahas tentang metode statistika ANOVA satu arah, meliputi pengertian ANOVA, kegunaan ANOVA, syarat-syarat menganalisis ANOVA, pengertian ANOVA satu arah, tujuan uji ANOVA satu arah, langkah-langkah uji ANOVA satu arah, dan contoh soal beserta pembahasannya.
Pengujian one way anova dengan manual dan spss 19Sowanto Sanusi
Teks tersebut membahas tentang analisis variansi (ANOVA) satu arah untuk menguji perbedaan rata-rata hasil belajar siswa pada lima model pembelajaran yang berbeda. Langkah-langkah ANOVA satu arah dijelaskan beserta contoh penyelesaiannya secara manual dan menggunakan SPSS. Hasilnya menunjukkan adanya perbedaan rata-rata hasil belajar antara kelima model pembelajaran.
Metode penelitian korelasional adalah metode untuk mengetahui hubungan antara dua variabel atau lebih tanpa manipulasi variabel. Tujuannya adalah mengukur tingkat hubungan antara variabel-variabel tersebut. Langkah-langkahnya meliputi penentuan masalah, studi pustaka, pertanyaan penelitian, rancangan penelitian, pengumpulan dan analisis data, serta kesimpulan. Metode ini memiliki kelebihan seperti dap
Analisis korelasi berganda digunakan untuk menguji hubungan simultan antara dua variabel independen atau lebih dengan satu variabel dependen. Ringkasan langkah-langkahnya adalah: (1) menginput data ke SPSS, (2) melakukan korelasi bivariat dan regresi linier, (3) menganalisis hasil untuk mengetahui besarnya hubungan dan kontribusi antar variabel. Contohnya menguji hubungan kompetensi dan motivasi terhadap k
Teks tersebut memberikan penjelasan tentang analisis uji asumsi klasik dan analisis regresi linear berganda dengan menggunakan aplikasi SPSS. Teks tersebut menjelaskan beberapa uji asumsi klasik yang sering digunakan seperti uji normalitas, multikolinearitas, heteroskedastisitas, autokorelasi dan linearitas serta cara melakukan uji-uji tersebut dengan SPSS.
Dokumen tersebut membahas tentang analisis korelasi dan regresi linear sederhana. Terdapat beberapa poin penting yaitu penjelasan tentang tujuan mempelajari regresi untuk memprediksi hubungan antar variabel, penggunaan diagram scatter plot dan rumus-rumus untuk menentukan persamaan regresi linear.
Dokumen tersebut berisi ringkasan hasil pengolahan data statistik menggunakan SPSS untuk berbagai uji statistik seperti uji t, ANOVA satu faktor, korelasi dan regresi berganda.
Panduan Lengkap Analisis Statistika dengan Aplikasi SPSSMuliadin Forester
Dokumen tersebut memberikan panduan lengkap mengenai analisis statistika menggunakan perangkat lunak SPSS (Statistical Package for Social Science), mencakup uji asumsi klasik seperti uji normalitas, multikolinearitas, heteroskedastisitas, autokorelasi, dan linearitas; serta analisis regresi linear berganda dan tabel statistik. Panduan ini disadur dari beberapa situs web dan disederhanakan untuk kemudahan pemahaman.
Dokumen tersebut membahas tentang regresi linier sederhana dan korelasi. Ia menjelaskan konsep dasar regresi dan korelasi, rumus-rumus dasar untuk menentukan persamaan regresi linier sederhana dan menghitung koefisien korelasi serta koefisien determinasi, beserta contoh penerapannya. Diberikan pula soal latihan dan kuis singkat untuk memahami konsep-konsep tersebut.
The document contains the results of several econometric analyses conducted by a student named Yolandafitri Zulvia.
1) An OLS regression of log wage on education, experience, and experience squared finds that all variables are statistically significant with experience having a diminishing positive effect on wage as it increases.
2) A regression of log housing price on log lot size, log square footage, and bedrooms finds all variables statistically significant with the exception of bedrooms.
3) Additional regressions examine the effect of race, location and other demographic variables on log wage, with results indicating blacks earn approximately 19% less than non-blacks, while those in the south earn around 9% less. Tests
The document summarizes the results of an experiment on leaching lead sulfide (PbS) particles using ferric chloride (FeCl3) as the lixiviant. The experiment involved leaching PbS at different times and temperatures, collecting leachate samples, and analyzing them to determine the amount of lead dissolved using atomic absorption spectroscopy. Graphs and tables show the results of lead concentration, mass of lead leached, and fractional lead dissolution over time for the different experimental conditions.
Uji BNT (Beda Nyata Terkecil) digunakan untuk membandingkan perbedaan rata-rata perlakuan. Uji ini menentukan nilai kritis untuk membandingkan selisih rata-rata antar perlakuan dan menentukan apakah perbedaan tersebut nyata secara statistik. Contoh penggunaan uji BNT untuk menguji pengaruh beberapa sistem olah tanah terhadap hasil kentang menunjukkan sistem olah tanah A memberikan hasil tertinggi sedang
Contoh soal spss independent dan one way anovapika setiawan
Berdasarkan hasil pengujian statistik independen sample t-test dan one-way ANOVA, terdapat perbedaan rata-rata pengetahuan komputer mahasiswa AKMI dan UNBARA serta minat calon mahasiswa antara sekolah kesehatan Alma'arif, Fatiah, dan Politeknik di Baturaja. H0 ditolak pada kedua uji statistik yang menunjukkan adanya perbedaan signifikan.
12 kul dan-responsi-uji-lanjut-multiple-comparison-testsardillah15
Dokumen tersebut membahas pengaruh konsentrasi bahan pengembang (foaming agent) terhadap overrun es krim. Ada empat konsentrasi yang diuji yaitu 0, 100, 200, dan 300 ppm. Jika rancangan yang digunakan adalah RAL, model liniernya adalah Yij = μ + αi + εij, dengan Yij adalah overrun es krim, μ adalah rata-rata, αi adalah pengaruh konsentrasi ke-i, dan εij adalah gal
Dokumen tersebut membahas tentang metode statistika ANOVA satu arah, meliputi pengertian ANOVA, kegunaan ANOVA, syarat-syarat menganalisis ANOVA, pengertian ANOVA satu arah, tujuan uji ANOVA satu arah, langkah-langkah uji ANOVA satu arah, dan contoh soal beserta pembahasannya.
Pengujian one way anova dengan manual dan spss 19Sowanto Sanusi
Teks tersebut membahas tentang analisis variansi (ANOVA) satu arah untuk menguji perbedaan rata-rata hasil belajar siswa pada lima model pembelajaran yang berbeda. Langkah-langkah ANOVA satu arah dijelaskan beserta contoh penyelesaiannya secara manual dan menggunakan SPSS. Hasilnya menunjukkan adanya perbedaan rata-rata hasil belajar antara kelima model pembelajaran.
Metode penelitian korelasional adalah metode untuk mengetahui hubungan antara dua variabel atau lebih tanpa manipulasi variabel. Tujuannya adalah mengukur tingkat hubungan antara variabel-variabel tersebut. Langkah-langkahnya meliputi penentuan masalah, studi pustaka, pertanyaan penelitian, rancangan penelitian, pengumpulan dan analisis data, serta kesimpulan. Metode ini memiliki kelebihan seperti dap
Analisis korelasi berganda digunakan untuk menguji hubungan simultan antara dua variabel independen atau lebih dengan satu variabel dependen. Ringkasan langkah-langkahnya adalah: (1) menginput data ke SPSS, (2) melakukan korelasi bivariat dan regresi linier, (3) menganalisis hasil untuk mengetahui besarnya hubungan dan kontribusi antar variabel. Contohnya menguji hubungan kompetensi dan motivasi terhadap k
Teks tersebut memberikan penjelasan tentang analisis uji asumsi klasik dan analisis regresi linear berganda dengan menggunakan aplikasi SPSS. Teks tersebut menjelaskan beberapa uji asumsi klasik yang sering digunakan seperti uji normalitas, multikolinearitas, heteroskedastisitas, autokorelasi dan linearitas serta cara melakukan uji-uji tersebut dengan SPSS.
Dokumen tersebut membahas tentang analisis korelasi dan regresi linear sederhana. Terdapat beberapa poin penting yaitu penjelasan tentang tujuan mempelajari regresi untuk memprediksi hubungan antar variabel, penggunaan diagram scatter plot dan rumus-rumus untuk menentukan persamaan regresi linear.
Dokumen tersebut berisi ringkasan hasil pengolahan data statistik menggunakan SPSS untuk berbagai uji statistik seperti uji t, ANOVA satu faktor, korelasi dan regresi berganda.
Panduan Lengkap Analisis Statistika dengan Aplikasi SPSSMuliadin Forester
Dokumen tersebut memberikan panduan lengkap mengenai analisis statistika menggunakan perangkat lunak SPSS (Statistical Package for Social Science), mencakup uji asumsi klasik seperti uji normalitas, multikolinearitas, heteroskedastisitas, autokorelasi, dan linearitas; serta analisis regresi linear berganda dan tabel statistik. Panduan ini disadur dari beberapa situs web dan disederhanakan untuk kemudahan pemahaman.
Dokumen tersebut membahas tentang regresi linier sederhana dan korelasi. Ia menjelaskan konsep dasar regresi dan korelasi, rumus-rumus dasar untuk menentukan persamaan regresi linier sederhana dan menghitung koefisien korelasi serta koefisien determinasi, beserta contoh penerapannya. Diberikan pula soal latihan dan kuis singkat untuk memahami konsep-konsep tersebut.
The document contains the results of several econometric analyses conducted by a student named Yolandafitri Zulvia.
1) An OLS regression of log wage on education, experience, and experience squared finds that all variables are statistically significant with experience having a diminishing positive effect on wage as it increases.
2) A regression of log housing price on log lot size, log square footage, and bedrooms finds all variables statistically significant with the exception of bedrooms.
3) Additional regressions examine the effect of race, location and other demographic variables on log wage, with results indicating blacks earn approximately 19% less than non-blacks, while those in the south earn around 9% less. Tests
The document summarizes the results of an experiment on leaching lead sulfide (PbS) particles using ferric chloride (FeCl3) as the lixiviant. The experiment involved leaching PbS at different times and temperatures, collecting leachate samples, and analyzing them to determine the amount of lead dissolved using atomic absorption spectroscopy. Graphs and tables show the results of lead concentration, mass of lead leached, and fractional lead dissolution over time for the different experimental conditions.
This document describes the results of a factorial design experiment with 3 factors (Fill Rate, Ramp Rate, Suck Back) each at 2 levels. It provides details of the experimental design such as the number of runs and replicates. It presents the effects and coefficients from the factorial regression analysis and identifies Suck Back as having the largest effect on the response (Means of repeats). Residual plots indicate a good model fit except for one outlier. Reduced models removing non-significant terms like Ramp Rate are also examined.
1) The document provides model solutions to questions from JEE Advanced 2013 Paper II.
2) It includes the answers to multiple choice and numerical questions across various sections - mechanics, electricity & magnetism, atomic structure, thermodynamics etc.
3) The solutions show the step-by-step working for calculating numerical values or explaining concepts to arrive at the correct answer option.
This document contains data and calculations related to linear regression analysis. It includes regression equations, calculations of mean and standard deviation, and use of Cramer's rule to determine regression coefficients from sample data. Regression lines are fitted to several data sets to determine the relationships between variables.
Determination of benzotriazoles in water samples by polyethersulfone solid-ph...Jorge Casado Agrelo
In this work, we investigate the suitability of a commercial available and low cost polyethersufone (PES) sorbent for the microextraction of 1H-benzotriazole (BTri), and four polar derivatives (4 and 5-methyl-1H-benzotriazole, 4-TTri and 5-TTri; 5,6-dimethyl-1H benzotriazole, XTri; and 5-chloro-1H-benzotriazole, 5-ClBTri) from surface and wastewater samples. The performance of liquid chromatography (LC) combined with quadrupole time-of-flight mass spectrometry (QTOF-MS) for the selective determination of target compounds is also discussed. Parameters affecting the efficiency of the microextraction step, such as sample’s pH, ionic strength, stirring speed and extraction lapse of time, and the PES membrane desorption process have been thoroughly investigated. Analytes were extracted from 15 mL samples, containing a 30% of sodium chloride and adjusted at pH 4.5, using a tubular PES sorbent (5 cm length x 0.7 mm o.d., sorbent volume 42 μL). After methanol desorption and solvent exchange, benzotriazoles were determined by LC-MS, with chromatograms extracted using a mass window of 20 ppm, centered in their [M+H]+ ions. The identity of chromatographic peaks was confirmed with accurate ion product scan (MS/MS) spectra. The method provided limits of quantification (LOQs) between 0.005 and 0.1 ng mL-1, and relative recoveries from 81% to 124% (except for XTri in sewage samples, ca. 60%) with associated standard deviations between 2% and 9%. The efficiency of the PES sorbent for the extraction of these compounds has been compared with that attained by stir-bar sorptive extraction (SBSE), with polydimethylsiloxane (PDMS) covered stir bars. The PES polymer achieved significant higher responses (5- to 20-fold) for these polar pollutants. To the best of our knowledge, this research constitutes the first application of both techniques (microextraction using a PES sorbent and LC-QTOF-MS) for benzotriazoles determination in water samples. The method was used to provide data regarding the levels of target compounds in river and urban wastewater samples, including the individual quantification of 4-methyl and 5-methyl-benzotriazole isomers. Obtained results confirmed the ubiquity of benzotriazole, 4-methyl and 5-methyl-benzotriazole in urban wastewater and their incomplete removal at sewage treatment plants
The document contains data arranged in tables with columns for variables x, y, f, x^2, etc. It discusses calculating means, standard deviations, and fitting distributions such as normal and lognormal to the data. It also contains examples of using the method of least squares to fit linear and quadratic regression models to data.
This document summarizes an experiment measuring detector efficiency. It found the effective distance of the detector using sodium-22 data to be 24.748 cm. It then measured efficiency as a function of distance and energy. Efficiency decreased with distance from the detector and followed a power trendline with decreasing negative log efficiency and increasing log energy. The document used various isotopes including sodium-22, cesium-137, cobalt-60, and bismuth-207 to calibrate and determine detector efficiency and calculate activity. It concluded that for a 1000 keV gamma ray, Compton scattering would be a more probable interaction than the photoelectric effect.
The document presents calculations and data from a copper flotation process. It includes equations for the various flotation circuits (rougher, scavenger, cleaner, etc.). Mass balances are provided showing the distribution of copper, silver, and iron throughout the process, from the feed to each cell to the final concentrate. Assays are given for the feed and outputs from each cell, along with calculated flow rates and recoveries.
The document describes a case study to optimize the process parameters for PCB wave soldering to minimize defects. The present defect level is 6920 ppm. Factors like flux SG, preheat temperature, solder temperature and conveyor speed were identified and their levels selected. Experiments were conducted using L16 orthogonal array and response in terms of defects was recorded. Data analysis using ANOVA identified flux SG, preheat temperature, solder temperature and their interactions as significant factors influencing soldering quality.
Diseno en ingenieria mecanica de Shigley - 8th ---HDes
descarga el contenido completo de aqui http://paralafakyoumecanismos.blogspot.com.ar/2014/08/libro-para-mecanismos-y-elementos-de.html
The document provides solutions to calculating various statistical measures - arithmetic mean, median, mode, harmonic mean, and geometric mean - for 5 sets of data. For each data set, the document calculates the measures using the relevant formulas. The statistical measures included arithmetic mean, median, mode, harmonic mean, and geometric mean. Formulas are provided for calculating each measure.
Sheet1stateviolentmurdermetrowhitehsgradpovertysnglparviofitvioresAK761941.875.286.69.114.3715.1390.2929492AL78011.667.473.566.917.411.5691.56320.4937207AR59310.244.782.966.32010.7453.88850.7989318AZ7158.684.788.678.715.412.1871.0881-0.872175CA107813.196.779.376.218.212.51067.5030.0599926CO5675.881.892.584.49.912.1751.1429-1.043932CT4566.395.78979.28.510.1570.3966-0.6560233DE686582.779.477.510.211.4670.80730.0854869FL12068.99383.574.417.810.6779.88882.470016GA72311.467.770.870.913.513823.5709-0.5653531HI2613.874.740.980.189.1264.7408-0.0212222IA3262.343.896.680.110.3950.250431.564434ID2822.93096.779.713.19.557.919851.284314IL96011.4848176.213.611.5754.33861.147723IN4897.571.690.675.612.210.8539.8218-0.2825857KS4966.454.690.981.313.19.9303.47481.074898KY4636.648.591.864.620.410.6477.4698-0.0833644LA106220.37566.768.326.414.91360.373-1.793716MA8053.996.291.18010.710.9719.1340.4875998MD99812.792.868.978.49.712820.48431.012708ME1261.635.798.578.810.710.6205.7611-0.4580154MI7929.882.783.176.815.413974.5974-1.022228MN3273.469.39482.411.69.9392.0398-0.3628658MO74411.368.387.673.916.110.9596.18010.8240324MS43413.530.763.364.324.714.7957.0128-3.193796MT17832492.68114.910.8214.9012-0.2136206NC67911.366.375.27014.411.1576.94740.5662267ND821.741.694.276.711.28.4-30.50590.6415154NE3393.950.694.381.810.39.4156.45031.028228NH138259.49882.29.99.2191.7913-0.3023897NJ6275.310080.876.710.99.6580.28960.2696506NM93085687.175.117.413.8906.85190.131264NV87510.484.886.778.89.812.4812.5840.3557735NY107413.391.777.274.816.412.71023.0160.2872973OH504681.387.575.71311.4709.3514-1.144457OK6358.460.182.574.619.911.1625.64940.0531835OR5034.67093.281.511.811.3586.4274-0.4646936PA4186.884.888.774.713.29.6501.9544-0.4756588RI4023.993.692.67211.210.8694.3781-1.653521SC102310.369.868.668.318.712.3839.26341.029668SD2083.432.690.277.114.29.484.482480.7058436TN76610.267.782.867.119.611.2693.08590.4139353TX76211.983.985.172.117.411.8860.463-0.5538287UT3013.177.594.885.110.710453.5657-0.8545464VA3728.377.577.175.29.710.3475.6079-0.5816147VT1143.62798.480.81011178.2364-0.379625WA5155.28389.483.812.111.7746.4708-1.294313WI2644.468.192.178.612.610.4466.5293-1.126045WV2086.941.896.36622.29.4297.9507-0.5456529WY2863.429.795.98313.310.8231.23770.3144581
Sheet2
Sheet3
For question 6 you should use the full dataset with all of the observations. To compare the observed and fitted values for those 3 observations you can use the 'list state metro.... if abs(viores)>2' command on the second page. And then to see if those observations have unusual explanatory or outcome values you can use the 'summ' command also on the second page.
For question 7, you first run the 'regr violent metro poverty snglpar' regression model on the full dataset and extract the information the question asks for from the output (R^2, root MSE, coefficient est, se). Then you drop the DC observation using the command on the 2nd page and rerun the regression model and extract the needed information from the output. You.
Diseno en ingenieria mecanica de Shigley - 8th ---HDes
descarga el contenido completo de aqui http://paralafakyoumecanismos.blogspot.com.ar/2014/08/libro-para-mecanismos-y-elementos-de.html
This document provides information for a 26.5 hectare agricultural project located at an altitude of 2500 meters above sea level. It includes a list of crops to be grown along with their areas, and requests calculations of water demand (module of irrigation) and design flow for hydraulic structures based on the provided crop data and efficiencies. Calculations will involve determining crop coefficients over time and monthly evapotranspiration values to estimate irrigation requirements.
1) The document presents the results of two GARCH models (GARCH(1,1) and GARCH(1,1)-AR(2)) fitted to RKLCI return data from 2000 to 2005.
2) Both models show no autoregression or heteroskedasticity based on diagnostic tests of the residuals.
3) Model 2, which includes an AR(2) term, has a slightly better fit based on information criteria.
The document provides material properties data from tables for various steels and metals. It includes yield strengths, ultimate tensile strengths, ductility values, and stiffness for different materials. Equations are also provided to calculate properties like specific strength and Poisson's ratio from the data. Graphs are plotted showing stress-strain curves and the relationship between yield strength and strain for one material.
1. The document analyzes conventional and Islamic monetary policy models in Indonesia from 1997 to 2003 using error correction models. It finds that changes in monetary aggregates (M1 and M2) are positively correlated with prior changes in the monetary base for both conventional and Islamic models.
2. Monetary aggregates are also found to be correlated with the main objective of monetary policy, inflation. Lags of changes in M1 and M2 are found to influence current inflation in both conventional and Islamic models.
3. Credit instruments are also analyzed, finding that lags of changes in credit are negatively correlated with the current change in credit for both conventional and Islamic models, indicating credit adjusts over time based on prior levels.
This document contains the results of three regressions analyzing the relationship between poverty (dependent variable) and income inequality and growth (independent variables) in 8 cross-sections over the period of 2002-2012. The normal regression shows a negative relationship between poverty and growth but the results are not statistically significant. The fixed effects regression finds a positive relationship between poverty and income inequality and negative relationship with growth. The random effects regression finds a negative relationship between poverty and growth but the other coefficients are not statistically significant.
2. Regression Results:
Answer 1
Table 1.1: Regression of logCO2 on logupopulation & logvehicledensity
Source SS Df MS Number of obs = 56
Model 39.2060945 2 19.6030473 F( 2, 53) = 30.73
Residual 33.8075327 53 0.637877975 Prob > F = 0
Total 73.0136272 55 1.32752049 R-squared = 0.537
Adj R-squared = 0.5195
Root MSE = 0.79867
logCO2 Coeff. Std.Err. t P>|t| [95% conf. Interval]
Logupop. 1.69305 0.3757207 4.51 0 .93945 2.44665
Logvden. 0.3391488 0.1278357 2.65 0.011 .0827429 .5955547
_cons -6.763162 1.366831 -4.95 0 -9.504678 -4.021647
Table 1.2: Mean of error terms
Mean Std.Err. [95% Conf. Interval]
e -3.04e-09 0.1047687 -.2099611 .2099611
Table 1.3(a): Correlation matrix of error terms & explanatory variables
e Logupop. Logvden.
e 1
Logupop. 0 1
Logvden. 0 0.5755 1
Table 1.3(b): VIF
Variable VIF 1/VIF
Logupop. 1.5 0.668764
Logvden. 1.5 0.668764
Mean 1.5
Table 1.4: Heteroskedascity Test
Breusch-Pagan / cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of logCO2
chi2(1) = 0.4
Prob > chi2 = 0.4923
*Logupop. = log(urbanpopulation) , logvden. = logvehicledensity
3. Table 1.5: Misspecification Test
Table 1.6: Normality of error terms
Jarque-Bera normality test: 0.7748 Chi(2) .6788
Jarque-Bera test for Ho: normality
Answer 2
Table 2.1: Regression of logCO2 on log(urbanpopulation), log(vehicledensity) & rich
Source SS df MS Number of obs = 56
Model 49.3314345 4 12.3328586 F( 3, 52) = 26.56
Residual 23.6821927 51 0.464356719 Prob > F = 0
Total 73.0136272 55 1.32752049 R-squared = 0.6756
Adj R-squared = 0.6502
Root MSE = 0.68144
logCO2 coeff. Std.Err. t P>|t| [95% Conf. Interval]
Logupop. 1.120989 0.3562969 3.15 0.003 .4056934 1.836285
Logvden. 2.4028 0.5250698 4.58 0.000 1.348679 3.456922
Rich 0.6663922 0.2399022 2.78 0.008 .1847685 1.148016
(Logvden.)2
-0.3036461 0.0733061 -4.14 0.000 -0.4508142 -.156478
_Cons -7.8182 1.426956 -5.48 0.000 -10.68294 -4.953466
Table 2.2: Mean of error terms
Mean Std.Err. [95% Conf. Interval]
e 1.31e-10 0.876871 -0.1757288 0.1757288
Table 2.3(a): correlation matrix of error terms & explanatory variables
e Logupop. Logvden. Rich (Logvden.)2
e 1
Logupop. -0.0000 1
Logvden. -0.0000 0.5755 1
Rich -0.0000 0.5726 0.4970 1
(Logvden.)2
-0.0000 0.5481 0.9844 0.5030 1
Ramsey RESET test using powers of the fitted values of logCO2
Ho: model has no omitted variables
F(3, 50) = 2.13
Prob > F = 0.1084
4. Table 2.3(b): VIF
Variable VIF 1/VIF
Logupop. 1.85 0.541369
Logvden. 34.65 0.028857
Rich 1.63 0.614730
(Logvden.)2
33.62 0.029747
Mean 17.94
Table 2.4: Heteroskedascity Test
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
H0: Constant variance
Variables: fitted values of logCO2
chi2(1) = 1.50
Prob > chi2 = 0.2209
Table 2.5: Misspecification Test
Ramsey RESET test using powers of the fitted values of logCO2
Ho: model has no omitted variables
F (3, 48) = 1.88
Prob > F = 0.1460
Table 2.6: Normality of errors
Jarque-Bera normality test: 2.485 Chi(2) 0.2886
Jarque-Bera test for Ho: normality
5. Answer 3
Table 3.1: Regression of logCO2 on log (urbanpopulation), log (vehicle density), dummies for Asia,
North America, South America, Oceania and Europe
Source SS df MS Number of obs = 56
Model 49.859338 7 7.12276257 F(7, 48) = 14.77
Residual 23.1542892 48 0.482381025 Prob > F = 0
Total 73.0136272 55 1.32752049 R-squared = 0.6829
Adj R-squared = 0.6366
Root MSE = 0.69454
logCO2 Coeff. Std.Err. t P>|t| [95% Conf. Interval]
Logupop. 1.366379 0.3838719 3.56 0.001 0.594553 2.138206
Logvden 0.2830142 0.1232192 2.3 0.026 0.0352654 0.530763
As 0.6984331 0.3815024 1.83 0.073 -0.0686288 1.465495
Na 0.4135816 0.4331655 0.95 0.344 -0.4573561 1.284519
Sa -0.3027211 0.4843232 -0.63 0.535 -1.276518 0.671076
Eu 1.048195 0.3373656 3.11 0.003 0.3698761 1.726514
Oc 1.349596 0.6169087 2.19 0.034 0.1092183 2.589975
_Cons -5.916017 1.290214 -4.59 0 -8.510166 -3.321868
Table 3.2: Mean of error terms
Mean Std. Err. [95% Conf. Interval]
e -2.18e-09 0.0867042 -0.1737592 0.1737592
Table 3.3(a): Correlation matrix of error terms & explanatory variables
e logupop logvden As Na Sa Oc Eu
e 1
Logupop 0 1
Logvden 0 0.5755 1
As 0 0.1218 0.288 1
Na 0 0.0021 0.0548 -0.1548 1
Sa 0 0.0641 -0.2177 -0.1371 -0.0868 1
Oc 0 0.1651 -0.0581 -0.0951 -0.0603 -0.0534 1
Eu 0 0.1452 0.0646 -0.4771 -0.3021 -0.2676 -0.1857 1
As = Asia, Na = North America, Sa = South America, Oc = Oceania, Eu = Europe
6. Table 3.3(b): VIF
Variable VIF 1/VIF
Eu 3.3 0.30312
As 2.67 0.374955
Logupop 2.06 0.484488
Logvden 1.84 0.544344
Sa 1.81 0.553662
Na 1.77 0.564586
Oc 1.52 0.657223
Mean 2.14
Table 3.4: Heteroskedascity
Table 3.5: Misspecification Test
Table 3.6: Normality of errors
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of logCO2
chi2(1) = 0.50
Prob > chi2 = 0.4801
Ramsey RESET test using powers of the fitted values of logCO2
Ho: model has no omitted variables
F (3, 45) = 2.40
Prob > F = 0.0803
Jarque-Bera normality test: .4714 Chi (2) .79
Jarque-Bera test for Ho: normality
7. Answer 4
Table 4.1: Regression logCO2, GDP, log(urbanpopulation), log(vehicledensity), Asia, South America,
North America, Europe, Oceania, North America1, South America1, Europeu1, Oceania1, Asia1
Source SS df MS Number of obs = 55
Model 57.6125472 13 4.4317344 F( 13, 41) = 11.8
Residual 15.4006539 41 0.375625705 Prob > F = 0
Total 73.0132011 54 1.35209632 R-squared = 0.7891
Adj R-squared = 0.7222
Root MSE = 0.61288
loglogCO2 Coeff. Std.Err. t P>|t| [95% Conf. Interval]
Logupop. 0.9552699 0.4046581 2.36 0.023 .1380463 1.772494
Logvden. 0.2605639 0.1275214 2.04 0.047 .0030292 .5180985
Asia 1.843853 0.5034198 3.66 0.001 .8271758 2.860529
Europe 1.954247 0.502562 3.89 0 .9393023 2.969191
NA 0.5495919 0.6296615 0.87 0.388 -.7220354 1.821219
Oceania 0.3393984 2.241271 0.15 0.88 -4.186941 4.865737
SA 0.760279 0.8204543 0.93 0.36 -.8966621 2.41722
As1 -0.0004321 0.0001546 -2.8 0.008 -.0007442 -.00012
Eu1 -0.0004167 0.0001592 -2.62 0.012 -.0007383 -.0000952
Na1 -0.0003575 0.0001609 -2.22 0.032 -.0006825 -.0000325
Oc1 -0.0003127 0.0001983 -1.58 0.122 -.0007131 .0000877
Sa1 -0.000426 0.0003179 -1.34 0.188 -.0010681 .000216
Gdp 0.0004284 0.0001609 2.66 0.011 .0001036 .0007533
_Cons -5.178985 1.368565 -3.78 0 -7.942857 -2.415112
Table 4.2: Mean of error terms
Mean Std.Err. [95% Conf. Interval]
e 9.57e-10 0.0720098 -.1443709 .1443709
As1 = Asia*Gdp, Na1 = North America*Gdp, Sa1 = South America*Gdp, Oc1 = Oceania*Gdp, Eu1 = Europe*Gdp
9. Table 4.3(b): VIF
Variable VIF 1/VIF
Gdp 564.93 0.00177
Eu1 520.21 0.001922
As1 156.21 0.006402
Na1 91.75 0.010899
Oc1 74.67 0.013392
Oc 25.77 0.038799
Eu 9.24 0.108197
Sa 6.65 0.150445
Sa1 6.01 0.166453
As 5.94 0.168427
Na 3.91 0.255431
Logupop. 2.93 0.341874
Logvden. 2.5 0.399485
Mean 113.13
Table 4.4: Heteroskedascity
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of logCO2
chi2(1) = 0.01
Prob > chi2 = 0.9078
Table 4.5: Misspecification Test
Ramsey RESET test using powers of the fitted values of logCO2
Ho: model has no omitted variables
F(3, 38) = 2.08
Prob > F = 0.1185
Table 4.6: Normality of errors
Jarque-Bera normality test: 2.886 Chi(2) .2362
Jarque-Bera test for Ho: normality
10. FIGURES
Figure 1: Plot of predicted errors from the regression of log(CO2) on log(urban population) and
log(vehicle density)
Figure 2: Plot of predicted errors from the regression of log(CO2) on rich dummy, log(urban
population), log(vehicle density) and [log(vehicle density)]2
0
.2.4.6
Density
-2 -1 0 1 2
Residuals
0
.2.4.6
Density
-1 0 1 2
Residuals
11. Figure 3: Plot of predicted errors from the regression of log(CO2) on continent dummies, log(urban
population) and log(vehicle density)
Figure 4: Plot of predicted errors from the regression of log(CO2) on GDP per capita, continent
dummies, interaction dummies, log(urban population) and log(vehicle density)
0
.2.4.6
Density
-2 -1 0 1 2
Residuals
0
.2.4.6.8
1
Density
-1 -.5 0 .5 1 1.5
Residuals