回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p>所以我有:</p>
<pre><code>t = [0.0, 3.0, 5.0, 7.2, 10.0, 13.0, 15.0, 20.0, 25.0, 30.0, 35.0]
U = [12.5, 10.0, 7.6, 6.0, 4.4, 3.1, 2.5, 1.5, 1.0, 0.5, 0.3]
U_0 = 12.5
y = []
for number in U:
y.<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>(math.log(number/U_0, math.e))
(m, b) = np.polyfit(t, y, 1)
yp = np.polyval([m, b], t)
plt.plot(t, yp)
plt.show()
</code></pre>
<p>通过这样做,我得到了<code>m=-0.1071</code>和<code>b=0.0347</code>的线性回归拟合。</p>
<p>如何得到m值的偏差或误差?</p>
<p>我想要<code>m = -0.1071*(1+ plus/minus error)</code></p>
<blockquote>
<p>m is k and b is n in y=kx+n</p>
</blockquote>