The Logarithmic Scale

Total Page:16

File Type:pdf, Size:1020Kb

The Logarithmic Scale IIEEEEROO nnlliinneCC llaassssrroooom| LL ooggaarriitthhmmiicSS ccaallee hhttttpp::////wwwwww..iieeeerr..oorrgg//ccllssssrroooomm//lloogg..hhttmmll IEER | | Online Classroom The Logarithmic Scale This doesn't refer to tapping out a beat on tree stumps, but it can be just as fun! If we want to plot something that changes with time and the time period is relatively short, we often use a linear scale. Thus if we were considering 1,000 years, the linear scale might look like this: Each tick mark represents 100 years and each subdivision of the scale would be the same length. 0 100 200 300 400 500 600 700 800 900 1000 |______|______|______|______|______|______|______|______|______|______| TIME (YEARS) When we consider the many thousands of years it will be necessary to store nuclear waste in a geologic repository, it would not be possible to represent the decay on a linear time scale. So we resort to a convenient device called the logarithmic scale for plotting large numbers. In the logarithmic scale the only line segments that are equal are those that represent multiples by a constant factor, such as 2 or 3 or 10. So 1,000 years on a simple logarithmic scale that showed only the broad divisions would look like this:: 1 10 100 1000 |___________________|___________________|__________________| 101000 101011 101022 101033 TIME (YEARS) As you can see, such a scale can plot a great many more years than is possible on a linear scale, but its use would be limited by its lack of detail. However, if we were to divide each broad segment into nine segments and let the ticks represent the years from 1 to 10, 10 to 100, and 100 to 1000, the scale would look like this and would be much more useful: Note that each broad segment is subdivided in the same way. Each tick within a broad segment represents a multiple of 10 over the corresponding tick in the previous segment. For example, in the segment 1000 to 1011 the first mark equals 2. In the segment 10 11 to 1022 the first mark equals 20, and in the segment 1022 to 1033, the first mark equals 200. Notice that the line segment between 10 and 20 is equal to that between 20 and 40, which is equal to that between 40 and 80. Similarly the segment between 10 and 30 is equal to that between 30 and 90. That's all there is to it! Try this worksheet on logarithms if you would like to practice your new skills! On-Line Classroom Main Page Institute for Energy and Environmental Research Comments to: ieer [at] ieer.org Updated March 2, 2001, and May 5, 2006 Linear vs logarithmic scales. http://www.cs.sfu.ca/~tamaras/digitalAudio/Linear_vs_logarithmic.html Decibels CMPT 889: Lecture 3 Fundamentals of Digital Audio, Discrete-Time Signals Sound Power and Intensity Linear vs logarithmic scales. Human hearing is better measured on a logarithmic scale than a linear scale. On a linear scale, a change between two values is perceived on the basis of the difference between the values. Thus, for example, a change from 1 to 2 would be perceived as the same amount of increase as from 4 to 5. On a logarithmic scale, a change between two values is perceived on the basis of the ratio of the two values. That is, a change from 1 to 2 (ratio of 1:2) would be perceived as the same amount of increase as a change from 4 to 8 (also a ratio of 1:2). Figure 1: Moving one unit to the right increment by 1 on the linear scale and multiplies by a factor of 10 on the logarithmic scale. Decibels CMPT 889: Lecture 3 Fundamentals of Digital Audio, Discrete-Time Signals Sound Power and Intensity ``CMPT 889: Lecture 3, Fundamentals of Digital Audio, Discrete-Time Signals '' by Tamara Smyth , Computing Science, Simon Fraser University. Download PDF version (digitalAudio.pdf) Download compressed PostScript version (digitalAudio.ps.gz) Download PDF `4 up' version (digitalAudio_4up.pdf) Download compressed PostScrip `4 up' version (digitalAudio_4up.ps.gz) Copyright © 2005-10-06 by Tamara Smyth. Please email errata, comments, and suggestions to Tamara Smyth<[email protected]> School of Computing Science, Simon Fraser University The difference between the Linear and the Logarithmic Scales | The Analyt... http://www.morevisibility.com/analyticsblog/the-difference-between-the-l... The difference between the Linear and the Logarithmic Scales Subscribe Login or Regi http://www.morevisibility.com/analyticsblog/the-difference-between-the-linear-and-the-logarithmic- scales.html Recent Articles Share | Google Releases Googlebo March 4th, 2009 by Joe Teixeira Mobile: Part I By now, mostly everyone has tried using a Motion Chart by clicking on the “Visualize” hat Questions Can Googl button, toward the top of several Google Analytics reports at least one time. Analytics Answer? Part II Are Your Website Visitors S No! I didn’t even know it existed! What? What do you mean “ “? What do you mean “ “? Engaged? Well, if you haven’t taken a look, I strongly suggest doing so. For example, go to your Traffic hat Questions Can Googl Sources >> Keywords report and click on “Visualize” towards the top – you should see Analytics Answer? something like this: Google Analytics Individual Qualification Update Google Announcement: Site Speed Google Analytics Metrics an Dimensions Tutorial: Regular Expressio Google Analytics: The Pipe Symbol Google Announcement: Sec Search Topsy: Google Plus Search Article Categories Web Analytics Google Analytics There you go – you have just been visualized! . You can increase the number of circles Google Website Optimizer (or “bubbles”) that appear by going back to the regular report and increasing the number of Web Analytics Metrics rows, toward the bottom-right of the report table. A/B Testing One update to Motion Charts that happened a few months ago which flew a bit under the Multivariate Testing radar was the option for you to define your viewing scale – Linear, or Logarithmic. I can get Site Usability into a complex mathematical / statistical explanation of the differences between the two – Surveys / Polls which I’m sure will satisfy some of you – but for the masses, the easiest possible explanation I can use to differentiate the two is: MSN Gatineau Yahoo! Analytics Linear Scale - Based on Addition AW Stats Logarithmic Scale - Based on Multiplication IndexTools The first image above is an example of a Motion Chart using the Linear Scale. N otice on the Webside Story (HBX) X axis (going from left to right, t he “Pages / Visit” metric), the values from left to right Feedburner increase by 5 on each point – 5, 10, 15, 20, etc The values in the Y axis (Visits) increase by 100 on each point – 100, 200, 300, and so on. Google AdWords Key Performance Indicators Now let’s change “Lin” to “ Log” and watch what happens to our Motion Chart: Omniture SiteCatalyst Tealeaf WebTrends Coremetrics MSN Analytics Omniture Test & Target Urchin Software from Googl Competitive Intelligence Google AdSense ClickTracks NetInsight Mobile Google Plus Articles by Month The motion chart data is exactly the same, but much different looking now, is it not? Notice January 2012 how the bubbles are much more spread out and more “all over the place” using the December 2011 Logarithmic Scale. The Motion Charts in Google Analytics use what is known as a base 10 November 2011 sequence , as each point in the scale is multiplied by 10. A little tough to notice here in the X axis (Pages / Visit), but noticeable on the Y axis (Visits) – 1, 10, 100, 1,000, and it October 2011 continues off the chart infinitely. September 2011 The difference between the Linear and the Logarithmic Scales | The Analyt... http://www.morevisibility.com/analyticsblog/the-difference-between-the-l... When to use the Linear Scale? When to use the Logarithmic Scale? August 2011 July 2011 A good rule of thumb is to use the scale that best shows off your data. If you are only using the motion chart for the top 10 rows of your report, chances are that the l inear scale will June 2011 work just fine. If you’re going to be using 25 or more rows, you’ll most likely find it much May 2011 easier on the eyes if you use the logarithmic scale. So, less data = linear; more data = April 2011 logarithmic. But, please, play with the Motion Charts and decide for yourself what’s easier March 2011 for your chart. February 2011 “The Difference Between” Series: January 2011 The difference between Bounce Rate and Exit Percentage December 2010 Why are my visits different from my clicks? November 2010 Why are Conversions in Google AdWords different than Goals in GA? October 2010 The difference between Landing Pages and Top Content Pages September 2010 The difference between Google Analytics and Urchin Software from Google August 2010 Posted in Google Analytics, Web Analytics, Web Analytics Metrics | No Comments » | July 2010 May 2010 April 2010 Comments are closed at this time. March 2010 February 2010 January 2010 November 2009 October 2009 September 2009 August 2009 July 2009 June 2009 May 2009 April 2009 March 2009 February 2009 January 2009 December 2008 November 2008 October 2008 September 2008 August 2008 July 2008 June 2008 May 2008 April 2008 March 2008 February 2008 Related Sites Web Analytics MoreVisibility SEM Blog MoreVisibility SEO Blog MoreVisibility Social Media What is the difference between a logarithmic price scale and a linear one? http://www.investopedia.com/ask/answers/05/logvslinear.asp Home Dictionary Articles Tutorials Exam Prep Forex Markets Simulator FinancialE dge Free Too enterkeywords entersymbol Which penny stocks will rise? We'll tell you, free! 1.
Recommended publications
  • Frequency Response and Bode Plots
    1 Frequency Response and Bode Plots 1.1 Preliminaries The steady-state sinusoidal frequency-response of a circuit is described by the phasor transfer function Hj( ) . A Bode plot is a graph of the magnitude (in dB) or phase of the transfer function versus frequency. Of course we can easily program the transfer function into a computer to make such plots, and for very complicated transfer functions this may be our only recourse. But in many cases the key features of the plot can be quickly sketched by hand using some simple rules that identify the impact of the poles and zeroes in shaping the frequency response. The advantage of this approach is the insight it provides on how the circuit elements influence the frequency response. This is especially important in the design of frequency-selective circuits. We will first consider how to generate Bode plots for simple poles, and then discuss how to handle the general second-order response. Before doing this, however, it may be helpful to review some properties of transfer functions, the decibel scale, and properties of the log function. Poles, Zeroes, and Stability The s-domain transfer function is always a rational polynomial function of the form Ns() smm as12 a s m asa Hs() K K mm12 10 (1.1) nn12 n Ds() s bsnn12 b s bsb 10 As we have seen already, the polynomials in the numerator and denominator are factored to find the poles and zeroes; these are the values of s that make the numerator or denominator zero. If we write the zeroes as zz123,, zetc., and similarly write the poles as pp123,, p , then Hs( ) can be written in factored form as ()()()s zsz sz Hs() K 12 m (1.2) ()()()s psp12 sp n 1 © Bob York 2009 2 Frequency Response and Bode Plots The pole and zero locations can be real or complex.
    [Show full text]
  • Computational Entropy and Information Leakage∗
    Computational Entropy and Information Leakage∗ Benjamin Fuller Leonid Reyzin Boston University fbfuller,[email protected] February 10, 2011 Abstract We investigate how information leakage reduces computational entropy of a random variable X. Recall that HILL and metric computational entropy are parameterized by quality (how distinguishable is X from a variable Z that has true entropy) and quantity (how much true entropy is there in Z). We prove an intuitively natural result: conditioning on an event of probability p reduces the quality of metric entropy by a factor of p and the quantity of metric entropy by log2 1=p (note that this means that the reduction in quantity and quality is the same, because the quantity of entropy is measured on logarithmic scale). Our result improves previous bounds of Dziembowski and Pietrzak (FOCS 2008), where the loss in the quantity of entropy was related to its original quality. The use of metric entropy simplifies the analogous the result of Reingold et. al. (FOCS 2008) for HILL entropy. Further, we simplify dealing with information leakage by investigating conditional metric entropy. We show that, conditioned on leakage of λ bits, metric entropy gets reduced by a factor 2λ in quality and λ in quantity. ∗Most of the results of this paper have been incorporated into [FOR12a] (conference version in [FOR12b]), where they are applied to the problem of building deterministic encryption. This paper contains a more focused exposition of the results on computational entropy, including some results that do not appear in [FOR12a]: namely, Theorem 3.6, Theorem 3.10, proof of Theorem 3.2, and results in Appendix A.
    [Show full text]
  • A Weakly Informative Default Prior Distribution for Logistic and Other
    The Annals of Applied Statistics 2008, Vol. 2, No. 4, 1360–1383 DOI: 10.1214/08-AOAS191 c Institute of Mathematical Statistics, 2008 A WEAKLY INFORMATIVE DEFAULT PRIOR DISTRIBUTION FOR LOGISTIC AND OTHER REGRESSION MODELS By Andrew Gelman, Aleks Jakulin, Maria Grazia Pittau and Yu-Sung Su Columbia University, Columbia University, University of Rome, and City University of New York We propose a new prior distribution for classical (nonhierarchi- cal) logistic regression models, constructed by first scaling all nonbi- nary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with cen- ter 0 and scale 2.5, which in the simplest setting is a longer-tailed version of the distribution attained by assuming one-half additional success and one-half additional failure in a logistic regression. Cross- validation on a corpus of datasets shows the Cauchy class of prior dis- tributions to outperform existing implementations of Gaussian and Laplace priors. We recommend this prior distribution as a default choice for rou- tine applied use. It has the advantage of always giving answers, even when there is complete separation in logistic regression (a common problem, even when the sample size is large and the number of pre- dictors is small), and also automatically applying more shrinkage to higher-order interactions. This can be useful in routine data analy- sis as well as in automated procedures such as chained equations for missing-data imputation. We implement a procedure to fit generalized linear models in R with the Student-t prior distribution by incorporating an approxi- mate EM algorithm into the usual iteratively weighted least squares.
    [Show full text]
  • 1 1. Data Transformations
    260 Archives ofDisease in Childhood 1993; 69: 260-264 STATISTICS FROM THE INSIDE Arch Dis Child: first published as 10.1136/adc.69.2.260 on 1 August 1993. Downloaded from 1 1. Data transformations M J R Healy Additive and multiplicative effects adding a constant quantity to the correspond- In the statistical analyses for comparing two ing before readings. Exactly the same is true of groups of continuous observations which I the unpaired situation, as when we compare have so far considered, certain assumptions independent samples of treated and control have been made about the data being analysed. patients. Here the assumption is that we can One of these is Normality of distribution; in derive the distribution ofpatient readings from both the paired and the unpaired situation, the that of control readings by shifting the latter mathematical theory underlying the signifi- bodily along the axis, and this again amounts cance probabilities attached to different values to adding a constant amount to each of the of t is based on the assumption that the obser- control variate values (fig 1). vations are drawn from Normal distributions. This is not the only way in which two groups In the unpaired situation, we make the further of readings can be related in practice. Suppose assumption that the distributions in the two I asked you to guess the size of the effect of groups which are being compared have equal some treatment for (say) increasing forced standard deviations - this assumption allows us expiratory volume in one second in asthmatic to simplify the analysis and to gain a certain children.
    [Show full text]
  • C-17Bpages Pdf It
    SPECIFICATION DEFINITIONS FOR LOGARITHMIC AMPLIFIERS This application note is presented to engineers who may use logarithmic amplifiers in a variety of system applica- tions. It is intended to help engineers understand logarithmic amplifiers, how to specify them, and how the loga- rithmic amplifiers perform in a system environment. A similar paper addressing the accuracy and error contributing elements in logarithmic amplifiers will follow this application note. INTRODUCTION The need to process high-density pulses with narrow pulse widths and large amplitude variations necessitates the use of logarithmic amplifiers in modern receiving systems. In general, the purpose of this class of amplifier is to condense a large input dynamic range into a much smaller, manageable one through a logarithmic transfer func- tion. As a result of this transfer function, the output voltage swing of a logarithmic amplifier is proportional to the input signal power range in dB. In most cases, logarithmic amplifiers are used as amplitude detectors. Since output voltage (in mV) is proportion- al to the input signal power (in dB), the amplitude information is displayed in a much more usable format than accomplished by so-called linear detectors. LOGARITHMIC TRANSFER FUNCTION INPUT RF POWER VS. DETECTED OUTPUT VOLTAGE 0V 50 MILLIVOLTS/DIV. 250 MILLIVOLTS/DIV. 0V 50.0 ns/DIV. 50.0 ns/DIV. There are three basic types of logarithmic amplifiers. These are: • Detector Log Video Amplifiers • Successive Detection Logarithmic Amplifiers • True Log Amplifiers DETECTOR LOG VIDEO AMPLIFIER (DLVA) is a type of logarithmic amplifier in which the envelope of the input RF signal is detected with a standard "linear" diode detector.
    [Show full text]
  • Linear and Logarithmic Scales
    Linear and logarithmic scales. Defining Scale You may have thought of a scale as something to weigh yourself with or the outer layer on the bodies of fish and reptiles. For this lesson, we're using a different definition of a scale. A scale, in this sense, is a leveled range of values/numbers from lowest to highest that measures something at regular intervals. A great example is the classic number line that has numbers lined up at consistent intervals along a line. What Is a Linear Scale? A linear scale is much like the number line described above. They key to this type of scale is that the value between two consecutive points on the line does not change no matter how high or low you are on it. For instance, on the number line, the distance between the numbers 0 and 1 is 1 unit. The same distance of one unit is between the numbers 100 and 101, or -100 and -101. However you look at it, the distance between the points is constant (unchanging) regardless of the location on the line. A great way to visualize this is by looking at one of those old school intro to Geometry or mid- level Algebra examples of how to graph a line. One of the properties of a line is that it is the shortest distance between two points. Another is that it is has a constant slope. Having a constant slope means that the change in x and y from one point to another point on the line doesn't change.
    [Show full text]
  • The Indefinite Logarithm, Logarithmic Units, and the Nature of Entropy
    The Indefinite Logarithm, Logarithmic Units, and the Nature of Entropy Michael P. Frank FAMU-FSU College of Engineering Dept. of Electrical & Computer Engineering 2525 Pottsdamer St., Rm. 341 Tallahassee, FL 32310 [email protected] August 20, 2017 Abstract We define the indefinite logarithm [log x] of a real number x> 0 to be a mathematical object representing the abstract concept of the logarithm of x with an indeterminate base (i.e., not specifically e, 10, 2, or any fixed number). The resulting indefinite logarithmic quantities naturally play a mathematical role that is closely analogous to that of dimensional physi- cal quantities (such as length) in that, although these quantities have no definite interpretation as ordinary numbers, nevertheless the ratio of two of these entities is naturally well-defined as a specific, ordinary number, just like the ratio of two lengths. As a result, indefinite logarithm objects can serve as the basis for logarithmic spaces, which are natural systems of logarithmic units suitable for measuring any quantity defined on a log- arithmic scale. We illustrate how logarithmic units provide a convenient language for explaining the complete conceptual unification of the dis- parate systems of units that are presently used for a variety of quantities that are conventionally considered distinct, such as, in particular, physical entropy and information-theoretic entropy. 1 Introduction The goal of this paper is to help clear up what is perceived to be a widespread arXiv:physics/0506128v1 [physics.gen-ph] 15 Jun 2005 confusion that can found in many popular sources (websites, popular books, etc.) regarding the proper mathematical status of a variety of physical quantities that are conventionally defined on logarithmic scales.
    [Show full text]
  • Logarithmic Amplifier for Ultrasonic Sensor Signal Conditioning
    Application Report SLDA053–March 2020 Logarithmic Amplifier for Ultrasonic Sensor Signal Conditioning Akeem Whitehead, Kemal Demirci ABSTRACT This document explains the basic operation of a logarithmic amplifier, how a logarithmic amplifier works in an ultrasonic front end, the advantages and disadvantages of using a logarithmic amplifier versus a linear time-varying gain amplifier, and compares the performance of a logarithmic amplifier versus a linear amplifier. These topics apply to the TUSS44x0 device family (TUSS4440 and TUSS4470), which includes Texas Instrument’s latest ultrasonic driver and logarithmic amplifier-based receiver front end integrated circuits. The receive signal path of the TUSS44x0 devices includes a low-noise linear amplifier, a band pass filter, followed by a logarithmic amplifier for input-level dependent amplification. The logarithmic amplifier allows for high sensitivity for weak echo signals and offers a wide input dynamic range over full range of reflected echoes. Contents 1 Introduction to Logarithmic Amplifier (Log Amp)......................................................................... 2 2 Logarithmic Amplifier in Ultrasonic Sensing.............................................................................. 8 3 Logarithmic versus Time Varying Linear Amplifier..................................................................... 10 4 Performance of Log Amp in Ultrasonic Systems....................................................................... 11 List of Figures 1 Linear Input and Logarithmic
    [Show full text]
  • Arxiv:1712.03037V1 [Cs.CV] 8 Dec 2017 Community
    A Frequency Domain Neural Network for Fast Image Super-resolution Junxuan Li1∗ Shaodi You1;2 Antonio Robles-Kelly1;2 1College of Eng. and Comp. Sci., Australian National University, Canberra, ACT 2601, Australia 2Datat61-CSIRO, Black Mountain Laboratories, Acton, ACT 2601, Australia Abstract by aggregating multiple frames with complementary spa- tial information or by relating the higher-resolved image to In this paper, we present a frequency domain neural net- the lower resolution one by a sparse linear system. For in- work for image super-resolution. The network employs the stance, Baker and Kanade [1] formulated the problem in a convolution theorem so as to cast convolutions in the spatial regularization setting where the examples are constructed domain as products in the frequency domain. Moreover, the using a pyramid approach. Protter et al. [22] used block non-linearity in deep nets, often achieved by a rectifier unit, matching to estimate a motion model and use exemplars to is here cast as a convolution in the frequency domain. This recover super-resolved videos. Yang et al. [31] used sparse not only yields a network which is very computationally ef- coding to perform super-resolution by learning a dictionary ficient at testing but also one whose parameters can all be that can then be used to produce the output image, by lin- learnt accordingly. The network can be trained using back early combining learned exemplars. propagation and is devoid of complex numbers due to the Note that, the idea of super-solution “by example” can use of the Hartley transform as an alternative to the Fourier be viewed as hinging on the idea of learning functions so transform.
    [Show full text]
  • Lecture 1-2: Sound Pressure Level Scale
    Lecture 1-2: Sound Pressure Level Scale Overview 1. Psychology of Sound. We can define sound objectively in terms of its physical form as pressure variations in a medium such as air, or we can define it subjectively in terms of the sensations generated by our hearing mechanism. Of course the sensation we perceive from a sound is linked to the physical properties of the sound wave through our hearing mechanism, whereby some aspects of the pressure variations are converted into firings in the auditory nerve. The study of how the objective physical character of sound gives rise to subjective auditory sensation is called psychoacoustics. The three dimensions of our psychological sensation of sound are called Loudness , Pitch and Timbre . 2. How can we measure the quantity of sound? The amplitude of a sound is a measure of the average variation in pressure, measured in pascals (Pa). Since the mean amplitude of a sound is zero, we need to measure amplitude either as the distance between the most negative peak and the most positive peak or in terms of the root-mean-square ( rms ) amplitude (i.e. the square root of the average squared amplitude). The power of a sound source is a measure of how much energy it radiates per second in watts (W). It turns out that the power in a sound is proportional to the square of its amplitude. The intensity of a sound is a measure of how much power can be collected per unit area in watts per square metre (Wm -2). For a sound source of constant power radiating equally in all directions, the sound intensity must fall with the square of the distance to the source (since the radiated energy is spread out through the surface of a sphere centred on the source, and the surface area of the sphere increases with the square of its radius).
    [Show full text]
  • Logarithmic System of Units
    Appendix A Logarithmic System of Units A.1 Introduction This appendix deals with the concepts and fundamentals of the logarithmic units and their applications in the engineering of radiowaves propagation. Use of these units, expressing the formulas, and illustrating figures/graphs in the logarithmic systems are common and frequently referred in the book. A.2 Definition According to the definition, logarithm of the ratio of two similar quantities in decimal base is called “bel,” and ten times of it is called “decibel.” = p bel log10 pr =[ ]= p decibel dB 10 log10 pr As the above formula suggests, it is a logarithmic scale, and due to the persistent usage of decimal base in the logarithmic units, it is normally omitted from the related symbol for simplicity. Since values in the logarithmic scale are the logarithm of the ratio of two similar quantities, hence they are dimensionless and measured in the same unit. In other words, the unit of denominator, pr, is the base of comparison and includes the required unit. To indicate the unit of the base in the logarithmic system of units, one or few characters are used according to the following examples: dBw: Decibel unit compared to 1 W power A. Ghasemi et al., Propagation Engineering in Radio Links Design, 523 DOI 10.1007/978-1-4614-5314-7, © Springer Science+Business Media New York 2013 524 A Logarithmic System of Units dBm: Decibel unit compared to 1 mW power dBkw: Decibel unit compared to 1 kW power Example A.1. Output power of a transmitter is 2 W, state its power in dBm,dBw, and dBkw.
    [Show full text]
  • Logistic Regression
    CHAPTER 5 Logistic regression Logistic regression is the standard way to model binary outcomes (that is, data yi that take on the values 0 or 1). Section 5.1 introduces logistic regression in a simple example with one predictor, then for most of the rest of the chapter we work through an extended example with multiple predictors and interactions. 5.1 Logistic regression with a single predictor Example: modeling political preference given income Conservative parties generally receive more support among voters with higher in- comes. We illustrate classical logistic regression with a simple analysis of this pat- tern from the National Election Study in 1992. For each respondent i in this poll, we label yi =1ifheorshepreferredGeorgeBush(theRepublicancandidatefor president) or 0 if he or she preferred Bill Clinton (the Democratic candidate), for now excluding respondents who preferred Ross Perot or other candidates, or had no opinion. We predict preferences given the respondent’s income level, which is characterized on a five-point scale.1 The data are shown as (jittered) dots in Figure 5.1, along with the fitted logistic regression line, a curve that is constrained to lie between 0 and 1. We interpret the line as the probability that y =1givenx—in mathematical notation, Pr(y =1x). We fit and display the logistic regression using the following R function calls:| fit.1 <- glm (vote ~ income, family=binomial(link="logit")) Rcode display (fit.1) to yield coef.est coef.se Routput (Intercept) -1.40 0.19 income 0.33 0.06 n=1179,k=2 residual deviance = 1556.9, null deviance = 1591.2 (difference = 34.3) 1 The fitted model is Pr(yi =1)=logit− ( 1.40 + 0.33 income).
    [Show full text]