introducing additional spatial channels that are exploited by using space-time coding. In this article, we survey the environmental factors that affect MIMO capacity. These factors include channel complexity, external interference, and channel estimation error. We discuss examples of space-time codes, including space-time low-density parity-check codes and spacetime turbo codes, and we investigate receiver approaches, including multichannel multiuser detection (MCMUD). The ‘multichannel’ term indicates that
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Statistics Multiple R 0.057961607 R Square 0.003359548 Adjusted R Square -0.012460142 Standard Error 1.08903837 Observations 65 ANOVA df SS MS F Significance F Regression 1 0.251866 0.251866 0.212365 0.646507 Residual 63 74.71829 1.186005 Total 64 74.97015 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.8423386 0.424293 11.41273 5.62E-17 3.994457 5.69022
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Crude Oil Price | A comprehensive examination of statistical models using Multiple Linear Regression | | STAT 378 | 4/29/2010 Introduction – definition of response, predictor, and indicator variables Our group has decided to explore the problem of rising crude oil prices and attempt to identify variables that contribute to rising/falling costs of oil roughly over the last 25 years. We have selected many different economic measurement tools that might contribute to how oil prices have
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Unknown Parameters (p+1) x 1 Y X e Independent Variables – n x(p+1) Error – nx1 4 Questions How many unknown parameters are there? Can you name them? How many populations will be sampled? What are conceptual populations? 5 Major Requirements for Doing a Regression Analysis The errors are normally distributed (not Y). Constant variance – What is the null hypothesis? Linear in the parameters Errors are independent. Some people call these assumptions. EY () X 6 Example
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Case of Salary discrimination based on Gender In this case the assertion has been made that the males of the population receive a higher salary than do females as a direct result of their gender. In order to test this assertion, we must first set up a hypothesis, and then test that hypothesis by analyzing the data provided. The first step in hypothesis testing is formalizing the hypothesis into a null hypothesis and an alternative hypothesis. The null hypothesis is a restatement of the assertion
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CHAPTER 1 INTRODUCTION 1.1 PRACTICUM INTRODUCTION According to Macmillan English Dictionary for Advanced Learners Second Edition (2011), practicum or practical means involving or relating to real situations and events. In the other words, it is the activity which not only about theory but facing the real world works. Students will be trained to apply the theory during this period. In Universiti Utara Malaysia (UUM), practicum is obligated for students in some courses as a fulfillment in the
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determination by x, y has a random component, u, which we call the ‘disturbance’ or ‘error’. Let i index the observations on the data pairs (x, y). The simple linear model formalizes the ideas just stated: yi = β0 + β1 xi + ui The parameters β0 and β1 represent the y-intercept and the slope of the relationship, respectively. In order to work with this model we need to make some assumptions about the behavior of the error term. For now we’ll assume three things: E(ui ) = 0 2 2 E(ui ) = σu E(ui u j ) =
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Hyperammonemia Ammonia is a normal constituent of all body fluids. At physiologic pH, it exists mainly as ammonium ion. Reference serum levels are less than 35 µmol/L. Excess ammonia is excreted as urea, which is synthesized in the liver through the urea cycle. Sources of ammonia include bacterial hydrolysis of urea and other nitrogenous compounds in the intestine, the purine-nucleotide cycle and amino acid transamination in skeletal muscle, and other metabolic processes in the kidneys and liver
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CASE: 32 - Overdue Bills CONTENTS 1) The Executive Summary a) Describe the most important Facts and Conclusions. 2) Introduction a) Purpose and Scope of Paper b) Questions of Interest, and/or hypotheses c) Describe the nature of the data set 3) Analysis and methods section a) Interpret the statistical summaries i) Tell the reader what you have found in the data (results, facts only). ii) Explain what those findings mean with regard to the problem (interpret results). b) Design –
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Running mean Absolute Deviation (RMAD) was computed by taking the average of two one-year-wide averages that are offset by one period relative to each other. Computing the ratio divided by the moving average in each period. Running sum of forecast errors were computed by taking the differences between the actual and the forecast demand for the periods being evaluated. | | | | | | Alpha | 0.2 | | | | Month | 1 | 2 | 3 | 4 | | Forecast | ABS Value | RMAD | RSFE | TS | 1 | | 39800
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